BiomarkerKB Wiki - User contributions [en]

BiomarkerKB Resource Integration

2025-12-16T16:20:11Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB, https://glycanage.com/publications, [https://www.cancergenomeinterpreter.org/biomarkers https://www.c], [https://github.com/issues/assigned?issue=clinical-biomarkers%7Cbiomarker-issue-repo%7C248 Glycan Biomarkers] ([https://github.com/glygener/CarboCurator code]), [https://www.alliancegenome.org/ Alliance Genome]

Please contact us at mazumder_lab@gwu.edu and daniallmasood@gwu.edu if you have any other resources that may contain biomarker data

= GWAS =
Status: Direct Integration into Data Model

* Published genome-wide association studies (GWAS).
* Provides biomarkers in form of SNPs.
* GWAS Catalog contains SNPs for a vast amount of diseases.
** Preliminary curation only focused on cancer.
** All available biomarkers for conditions in GWAS Catalog are integrated 12/11
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

= MetaKB =
Status: Direct Integration into Data Model

* Provides harmonized associations between cancer genomic variants, diseases, and therapeutic evidence.
* Aggregates and standardizes variant interpretation data from six major knowledgebases:
** CIViC (Clinical Interpretation of Variants in Cancer) [Already Integrated Directly]
** OncoKB [Yet to be integrated]
** JAX-CKB (The Jackson Laboratory Clinical Knowledgebase) [Yet to be integrated]
** MolecularMatch [Yet to be integrated]
** PMKB (Precision Medicine Knowledgebase) [Yet to be integrated]
** Cancer Genome Interpreter (CGI) – through its ''Cancer Biomarkers Database'' component .[Integrated]
* Enables mapping of variant–disease–drug relationships with supporting evidence levels, citations, and ontology alignment (e.g., genes, variants, diseases, and drugs).
* Data integration requires review to ensure harmonized entity mappings consistent with the BiomarkerKB data model.
* Focused on somatic variant–based biomarkers; contextual attributes such as tissue type, therapy response, or evidence type can be inferred or imputed where not directly specified.
* Manual curation may be required for entries with incomplete evidence annotation or lacking standard ontology references.
* Integration approach: direct mapping of variant, condition, and evidence entities; cross-references retained to original data sources.
* License: Aggregated data are available for non-commercial, research use only, respecting constituent licenses:
** CIViC – CC0 (Public Domain)
** PMKB – CC-BY 4.0
** CGI – CC0 for biomarkers database, CC-BY-NC 4.0 for tool
** JAX-CKB – CC-BY-NC-SA 4.0
** OncoKB – custom non-commercial license
** MolecularMatch – restricted commercial use
** MetaKB codebase – MIT license
* Overall usage requires adherence to non-commercial research terms; commercial use needs separate permissions from individual data providers.

= Glycan LLM Biomarkers =
* LangChain LLM method used to collect biomarkers from PubMed Central abstracts
* Method identifies glycan entities and changes mentioned in them associated to disease

= Top 50 Biomarkers =
Status: Direct Integration into Data Model
* Biomarkers collected during Summer Volunteership
* Volunteers identified top 50 biomarker entities from BiomarkerKB
* Using this information the top 50 biomarker entities were searched in PubMed
* 100 biomarkers were manually curated

*

= EDRN =
Status: Sample Integration into Data Model

* Cancer biomarkers.
* Sample of EDRN Biomarkers provided from EDRN LLM method
* Biomarkers are extracted from free text in EDRN publicly available biomarkers

= LOINC =
Status: Cross-Reference

''Data provided by Metabolomics Workbench''

= OncoKB =
Status: Cross-Reference

* Provides useful information on drugs and therapy options for different biomarker entities.
* Also provides information based on what condition the entity is related to.
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross-reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution.

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations.
* Does not provide a change within the entity so we cannot collect biomarker data from here.
* However we can use it as a cross-reference within our cross-referencing section.
* Provides cross-reference to OMIM, SNOMED, and MONDO.

= UniProtKB =
Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data.
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted.
* Contextual information can be imputed if necessary.
* In UniProt there are found_in and entries that are actual biomarkers:
** found_in will get a cross-reference;
** actual biomarkers will be directly integrated.
* Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file.
* License is Creative Commons Attribution 4.0 International (CC BY 4.0).

= CIViC =
Status: Direct Integration into Data Model

* Clinical Interpretation of Variants in Cancer (CIViC).
* Provides cancer biomarkers in form of DNA mutations (dbSNPs).
* Platform provides clinicians treatment options for patients based on unique tumor profile.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=ClinVar=
Status: Direct Integration into Data Model

* Public archive of reports of human variations classified for diseases and drug responses.
* Provides biomarkers for all disease, but we have only curated cancer biomarkers for now.
** dbSNPs
** File is really big but will go back and use existing script to map all biomarkers from here into the data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

= MarkerDB =
Status: Direct Integration into Data Model

* Provides a lot of useful biomarker data and cross-references other resources as well.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc.
* Annotations that can be cross-referenced include the above.
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=Metabolomics Workbench=
Status: Direct Integration into Data Model

''Data provided by Metabolomics Workbench''

* Metabolite biomarkers utilized in the uniform newborn screening program.
* Detect treatable disorders that are life threatening or having long-term morbidity, before they become symptomatic.

=OncoMX=
Status: Direct Integration into Data Model

* integrated cancer mutation and expression resource for exploring cancer biomarkers
* Manual curation effort by GWU and JPL
* Over 600 single and panel biomarkers
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=OpenTargets=
Status: Direct Integration into Data Model

* Collects potential drug targets and therapeutic targets.
* Some effort was required to find the correct biomarker data.
* 1200 biomarkers collected.
** dbSNPs related to cancer and other disease
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=PubMed Central Biomarker Gene Set Curation=
Status: Direct Integration into Data Model

''Data provided by Avi Ma'ayan's LINCS group''

* This data set was created through manual curation of biomarker gene sets on Pubmed Central using the results of gene sets returned from Rummagene.
* Using the outputted search results within the Rummagene web server, we manually identified publications that associated different conditions and environmental exposures to biomarker gene sets.
* The biomarker gene sets were retrieved through the validation of the gene mentioned within each of the publications.
* The primary use case for this data is to identify biomarker panels/ gene sets associated with conditions.

= SenNet Biomarker Data =
Status: Direction Integration Into Data Model

* Cell senescence biomarkers from SenNet group
* Biomarker data was collected and incorporated however biomarker field was incomplete and data integrated was given a score of -2
* Data is still valuable as contextual data and can be revisited to complete biomarker field in future

BiomarkerKB Resource Integration

2025-12-11T22:59:48Z

DaniallMasood: /* Glycan LLM Biomarkers */

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB, https://glycanage.com/publications, [https://www.cancergenomeinterpreter.org/biomarkers https://www.c], [https://github.com/issues/assigned?issue=clinical-biomarkers%7Cbiomarker-issue-repo%7C248 Glycan Biomarkers] ([https://github.com/glygener/CarboCurator code]), [https://www.alliancegenome.org/ Alliance Genome]

Please contact us at mazumder_lab@gwu.edu and daniallmasood@gwu.edu if you have any other resources that may contain biomarker data

=CIViC=
Status: Direct Integration into Data Model

* Clinical Interpretation of Variants in Cancer (CIViC).
* Provides cancer biomarkers in form of DNA mutations (dbSNPs).
* Platform provides clinicians treatment options for patients based on unique tumor profile.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=ClinVar=
Status: Direct Integration into Data Model

* Public archive of reports of human variations classified for diseases and drug responses.
* Provides biomarkers for all disease, but we have only curated cancer biomarkers for now.
** dbSNPs
** File is really big but will go back and use existing script to map all biomarkers from here into the data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=EDRN=
Status: Sample Integration into Data Model

* Cancer biomarkers.

=GWAS=
Status: Direct Integration into Data Model

* Published genome-wide association studies (GWAS).
* Provides biomarkers in form of SNPs.
* GWAS Catalog contains SNPs for a vast amount of diseases.
** Preliminary curation only focused on cancer.
** Will use existing script to map all biomarkers into data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

= Glycan LLM Biomarkers =

* LangChain LLM method used to collect biomarkers from PubMed Central abstracts
* Method identifies glycan entities and changes mentioned in them associated to disease

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations.
* Does not provide a change within the entity so we cannot collect biomarker data from here.
* However we can use it as a cross-reference within our cross-referencing section.
* Provides cross-reference to OMIM, SNOMED, and MONDO.

=LOINC=
Status: Cross-Reference

''Data provided by Metabolomics Workbench''

=MarkerDB=
Status: Direct Integration into Data Model

* Provides a lot of useful biomarker data and cross-references other resources as well.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc.
* Annotations that can be cross-referenced include the above.
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=Metabolomics Workbench=
Status: Direct Integration into Data Model

''Data provided by Metabolomics Workbench''

* Metabolite biomarkers utilized in the uniform newborn screening program.
* Detect treatable disorders that are life threatening or having long-term morbidity, before they become symptomatic.

=OncoKB=
Status: Cross-Reference

* Provides useful information on drugs and therapy options for different biomarker entities.
* Also provides information based on what condition the entity is related to.
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross-reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution.

=OncoMX=
Status: Direct Integration into Data Model

* integrated cancer mutation and expression resource for exploring cancer biomarkers
* Manual curation effort by GWU and JPL
* Over 600 single and panel biomarkers
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=OpenTargets=
Status: Direct Integration into Data Model

* Collects potential drug targets and therapeutic targets.
* Some effort was required to find the correct biomarker data.
* 1200 biomarkers collected.
** dbSNPs related to cancer and other disease
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=PubMed Central Biomarker Gene Set Curation=
Status: Direct Integration into Data Model

''Data provided by Avi Ma'ayan's LINCS group''

* This data set was created through manual curation of biomarker gene sets on Pubmed Central using the results of gene sets returned from Rummagene.
* Using the outputted search results within the Rummagene web server, we manually identified publications that associated different conditions and environmental exposures to biomarker gene sets.
* The biomarker gene sets were retrieved through the validation of the gene mentioned within each of the publications.
* The primary use case for this data is to identify biomarker panels/ gene sets associated with conditions.

= SenNet Biomarker Data =
Status: Direction Integration Into Data Model

* Cell senescence biomarkers from SenNet group
* Biomarker data was collected and incorporated however biomarker field was incomplete and data integrated was given a score of -2
* Data is still valuable as contextual data and can be revisited to complete biomarker field in future

= Top 50 Biomarkers =
Status: Direct Integration into Data Model
* Biomarkers collected during Summer Volunteership
* Volunteers identified top 50 biomarker entities from BiomarkerKB
* Using this information the top 50 biomarker entities were searched in PubMed
* 100 biomarkers were manually curated

=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data.
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted.
* Contextual information can be imputed if necessary.
* In UniProt there are found_in and entries that are actual biomarkers:
** found_in will get a cross-reference;
** actual biomarkers will be directly integrated.
* Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file.
* License is Creative Commons Attribution 4.0 International (CC BY 4.0).
=MetaKB=

Status: Direct Integration into Data Model

* Provides harmonized associations between cancer genomic variants, diseases, and therapeutic evidence.
* Aggregates and standardizes variant interpretation data from six major knowledgebases:
** CIViC (Clinical Interpretation of Variants in Cancer) [Already Integrated Directly]
** OncoKB [Yet to be integrated]
** JAX-CKB (The Jackson Laboratory Clinical Knowledgebase) [Yet to be integrated]
** MolecularMatch [Yet to be integrated]
** PMKB (Precision Medicine Knowledgebase) [Yet to be integrated]
** Cancer Genome Interpreter (CGI) – through its ''Cancer Biomarkers Database'' component .[Integrated]
* Enables mapping of variant–disease–drug relationships with supporting evidence levels, citations, and ontology alignment (e.g., genes, variants, diseases, and drugs).
* Data integration requires review to ensure harmonized entity mappings consistent with the BiomarkerKB data model.
* Focused on somatic variant–based biomarkers; contextual attributes such as tissue type, therapy response, or evidence type can be inferred or imputed where not directly specified.
* Manual curation may be required for entries with incomplete evidence annotation or lacking standard ontology references.
* Integration approach: direct mapping of variant, condition, and evidence entities; cross-references retained to original data sources.
* License: Aggregated data are available for non-commercial, research use only, respecting constituent licenses:
** CIViC – CC0 (Public Domain)
** PMKB – CC-BY 4.0
** CGI – CC0 for biomarkers database, CC-BY-NC 4.0 for tool
** JAX-CKB – CC-BY-NC-SA 4.0
** OncoKB – custom non-commercial license
** MolecularMatch – restricted commercial use
** MetaKB codebase – MIT license
* Overall usage requires adherence to non-commercial research terms; commercial use needs separate permissions from individual data providers.

BiomarkerKB Resource Integration

2025-12-11T16:32:22Z

DaniallMasood: /* Top 50 Biomarkers */

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB, https://glycanage.com/publications, [https://www.cancergenomeinterpreter.org/biomarkers https://www.c], [https://github.com/issues/assigned?issue=clinical-biomarkers%7Cbiomarker-issue-repo%7C248 Glycan Biomarkers] ([https://github.com/glygener/CarboCurator code]), [https://www.alliancegenome.org/ Alliance Genome]

Please contact us at mazumder_lab@gwu.edu and daniallmasood@gwu.edu if you have any other resources that may contain biomarker data

=CIViC=
Status: Direct Integration into Data Model

* Clinical Interpretation of Variants in Cancer (CIViC).
* Provides cancer biomarkers in form of DNA mutations (dbSNPs).
* Platform provides clinicians treatment options for patients based on unique tumor profile.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=ClinVar=
Status: Direct Integration into Data Model

* Public archive of reports of human variations classified for diseases and drug responses.
* Provides biomarkers for all disease, but we have only curated cancer biomarkers for now.
** dbSNPs
** File is really big but will go back and use existing script to map all biomarkers from here into the data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=EDRN=
Status: Sample Integration into Data Model

* Cancer biomarkers.

=GWAS=
Status: Direct Integration into Data Model

* Published genome-wide association studies (GWAS).
* Provides biomarkers in form of SNPs.
* GWAS Catalog contains SNPs for a vast amount of diseases.
** Preliminary curation only focused on cancer.
** Will use existing script to map all biomarkers into data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

= Glycan LLM Biomarkers =

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations.
* Does not provide a change within the entity so we cannot collect biomarker data from here.
* However we can use it as a cross-reference within our cross-referencing section.
* Provides cross-reference to OMIM, SNOMED, and MONDO.

=LOINC=
Status: Cross-Reference

''Data provided by Metabolomics Workbench''

=MarkerDB=
Status: Direct Integration into Data Model

* Provides a lot of useful biomarker data and cross-references other resources as well.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc.
* Annotations that can be cross-referenced include the above.
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=Metabolomics Workbench=
Status: Direct Integration into Data Model

''Data provided by Metabolomics Workbench''

* Metabolite biomarkers utilized in the uniform newborn screening program.
* Detect treatable disorders that are life threatening or having long-term morbidity, before they become symptomatic.

=OncoKB=
Status: Cross-Reference

* Provides useful information on drugs and therapy options for different biomarker entities.
* Also provides information based on what condition the entity is related to.
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross-reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution.

=OncoMX=
Status: Direct Integration into Data Model

* integrated cancer mutation and expression resource for exploring cancer biomarkers
* Manual curation effort by GWU and JPL
* Over 600 single and panel biomarkers
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=OpenTargets=
Status: Direct Integration into Data Model

* Collects potential drug targets and therapeutic targets.
* Some effort was required to find the correct biomarker data.
* 1200 biomarkers collected.
** dbSNPs related to cancer and other disease
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=PubMed Central Biomarker Gene Set Curation=
Status: Direct Integration into Data Model

''Data provided by Avi Ma'ayan's LINCS group''

* This data set was created through manual curation of biomarker gene sets on Pubmed Central using the results of gene sets returned from Rummagene.
* Using the outputted search results within the Rummagene web server, we manually identified publications that associated different conditions and environmental exposures to biomarker gene sets.
* The biomarker gene sets were retrieved through the validation of the gene mentioned within each of the publications.
* The primary use case for this data is to identify biomarker panels/ gene sets associated with conditions.

= SenNet Biomarker Data =

= Top 50 Biomarkers =

* Biomarkers collected during Summer Volunteership
* Volunteers

=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data.
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted.
* Contextual information can be imputed if necessary.
* In UniProt there are found_in and entries that are actual biomarkers:
** found_in will get a cross-reference;
** actual biomarkers will be directly integrated.
* Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file.
* License is Creative Commons Attribution 4.0 International (CC BY 4.0).
=MetaKB=

Status: Direct Integration into Data Model

* Provides harmonized associations between cancer genomic variants, diseases, and therapeutic evidence.
* Aggregates and standardizes variant interpretation data from six major knowledgebases:
** CIViC (Clinical Interpretation of Variants in Cancer) [Already Integrated Directly]
** OncoKB [Yet to be integrated]
** JAX-CKB (The Jackson Laboratory Clinical Knowledgebase) [Yet to be integrated]
** MolecularMatch [Yet to be integrated]
** PMKB (Precision Medicine Knowledgebase) [Yet to be integrated]
** Cancer Genome Interpreter (CGI) – through its ''Cancer Biomarkers Database'' component .[Integrated]
* Enables mapping of variant–disease–drug relationships with supporting evidence levels, citations, and ontology alignment (e.g., genes, variants, diseases, and drugs).
* Data integration requires review to ensure harmonized entity mappings consistent with the BiomarkerKB data model.
* Focused on somatic variant–based biomarkers; contextual attributes such as tissue type, therapy response, or evidence type can be inferred or imputed where not directly specified.
* Manual curation may be required for entries with incomplete evidence annotation or lacking standard ontology references.
* Integration approach: direct mapping of variant, condition, and evidence entities; cross-references retained to original data sources.
* License: Aggregated data are available for non-commercial, research use only, respecting constituent licenses:
** CIViC – CC0 (Public Domain)
** PMKB – CC-BY 4.0
** CGI – CC0 for biomarkers database, CC-BY-NC 4.0 for tool
** JAX-CKB – CC-BY-NC-SA 4.0
** OncoKB – custom non-commercial license
** MolecularMatch – restricted commercial use
** MetaKB codebase – MIT license
* Overall usage requires adherence to non-commercial research terms; commercial use needs separate permissions from individual data providers.

BiomarkerKB Resource Integration

2025-12-11T16:31:42Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB, https://glycanage.com/publications, [https://www.cancergenomeinterpreter.org/biomarkers https://www.c], [https://github.com/issues/assigned?issue=clinical-biomarkers%7Cbiomarker-issue-repo%7C248 Glycan Biomarkers] ([https://github.com/glygener/CarboCurator code]), [https://www.alliancegenome.org/ Alliance Genome]

Please contact us at mazumder_lab@gwu.edu and daniallmasood@gwu.edu if you have any other resources that may contain biomarker data

=CIViC=
Status: Direct Integration into Data Model

* Clinical Interpretation of Variants in Cancer (CIViC).
* Provides cancer biomarkers in form of DNA mutations (dbSNPs).
* Platform provides clinicians treatment options for patients based on unique tumor profile.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=ClinVar=
Status: Direct Integration into Data Model

* Public archive of reports of human variations classified for diseases and drug responses.
* Provides biomarkers for all disease, but we have only curated cancer biomarkers for now.
** dbSNPs
** File is really big but will go back and use existing script to map all biomarkers from here into the data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=EDRN=
Status: Sample Integration into Data Model

* Cancer biomarkers.

=GWAS=
Status: Direct Integration into Data Model

* Published genome-wide association studies (GWAS).
* Provides biomarkers in form of SNPs.
* GWAS Catalog contains SNPs for a vast amount of diseases.
** Preliminary curation only focused on cancer.
** Will use existing script to map all biomarkers into data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

= Glycan LLM Biomarkers =

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations.
* Does not provide a change within the entity so we cannot collect biomarker data from here.
* However we can use it as a cross-reference within our cross-referencing section.
* Provides cross-reference to OMIM, SNOMED, and MONDO.

=LOINC=
Status: Cross-Reference

''Data provided by Metabolomics Workbench''

=MarkerDB=
Status: Direct Integration into Data Model

* Provides a lot of useful biomarker data and cross-references other resources as well.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc.
* Annotations that can be cross-referenced include the above.
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=Metabolomics Workbench=
Status: Direct Integration into Data Model

''Data provided by Metabolomics Workbench''

* Metabolite biomarkers utilized in the uniform newborn screening program.
* Detect treatable disorders that are life threatening or having long-term morbidity, before they become symptomatic.

=OncoKB=
Status: Cross-Reference

* Provides useful information on drugs and therapy options for different biomarker entities.
* Also provides information based on what condition the entity is related to.
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross-reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution.

=OncoMX=
Status: Direct Integration into Data Model

* integrated cancer mutation and expression resource for exploring cancer biomarkers
* Manual curation effort by GWU and JPL
* Over 600 single and panel biomarkers
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=OpenTargets=
Status: Direct Integration into Data Model

* Collects potential drug targets and therapeutic targets.
* Some effort was required to find the correct biomarker data.
* 1200 biomarkers collected.
** dbSNPs related to cancer and other disease
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=PubMed Central Biomarker Gene Set Curation=
Status: Direct Integration into Data Model

''Data provided by Avi Ma'ayan's LINCS group''

* This data set was created through manual curation of biomarker gene sets on Pubmed Central using the results of gene sets returned from Rummagene.
* Using the outputted search results within the Rummagene web server, we manually identified publications that associated different conditions and environmental exposures to biomarker gene sets.
* The biomarker gene sets were retrieved through the validation of the gene mentioned within each of the publications.
* The primary use case for this data is to identify biomarker panels/ gene sets associated with conditions.

= SenNet Biomarker Data =

= Top 50 Biomarkers =

* Biomarkers collected during Summer Volunteership

=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data.
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted.
* Contextual information can be imputed if necessary.
* In UniProt there are found_in and entries that are actual biomarkers:
** found_in will get a cross-reference;
** actual biomarkers will be directly integrated.
* Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file.
* License is Creative Commons Attribution 4.0 International (CC BY 4.0).
=MetaKB=

Status: Direct Integration into Data Model

* Provides harmonized associations between cancer genomic variants, diseases, and therapeutic evidence.
* Aggregates and standardizes variant interpretation data from six major knowledgebases:
** CIViC (Clinical Interpretation of Variants in Cancer) [Already Integrated Directly]
** OncoKB [Yet to be integrated]
** JAX-CKB (The Jackson Laboratory Clinical Knowledgebase) [Yet to be integrated]
** MolecularMatch [Yet to be integrated]
** PMKB (Precision Medicine Knowledgebase) [Yet to be integrated]
** Cancer Genome Interpreter (CGI) – through its ''Cancer Biomarkers Database'' component .[Integrated]
* Enables mapping of variant–disease–drug relationships with supporting evidence levels, citations, and ontology alignment (e.g., genes, variants, diseases, and drugs).
* Data integration requires review to ensure harmonized entity mappings consistent with the BiomarkerKB data model.
* Focused on somatic variant–based biomarkers; contextual attributes such as tissue type, therapy response, or evidence type can be inferred or imputed where not directly specified.
* Manual curation may be required for entries with incomplete evidence annotation or lacking standard ontology references.
* Integration approach: direct mapping of variant, condition, and evidence entities; cross-references retained to original data sources.
* License: Aggregated data are available for non-commercial, research use only, respecting constituent licenses:
** CIViC – CC0 (Public Domain)
** PMKB – CC-BY 4.0
** CGI – CC0 for biomarkers database, CC-BY-NC 4.0 for tool
** JAX-CKB – CC-BY-NC-SA 4.0
** OncoKB – custom non-commercial license
** MolecularMatch – restricted commercial use
** MetaKB codebase – MIT license
* Overall usage requires adherence to non-commercial research terms; commercial use needs separate permissions from individual data providers.

Biomarker Aspect Terms Controlled Vocabulary

2025-12-10T15:53:24Z

DaniallMasood:

[[Biomarker Entity Types Controlled Vocabulary]]

[[Biomarker Reporting Terms Controlled Vocabulary]]

[[Biomarker Modifications Terms Controlled Vocabulary]]

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Aspect Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of standardized aspects used to describe

the specific measurable or biological feature of a biomarker entity.

Name: aspect.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists controlled terms that represent measurable or

definable aspects of a biomarker entity. These terms indicate what

property, modification, or characteristic of the biomarker is being

evaluated, enabling structured representation of natural language

phrases such as "increased IL6 level" or "presence of BRCA2 mutation".

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Aspect term Once; starts an entry

AC Accession Unique identifier (AS-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID level

AC AS-0001

DE A position on a scale measuring intensity, quality, or amount.

EQ NCIT:C25554; Level

EX increased IL6 level; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID expression

AC AS-0002

DE Production level or transcriptional activity of a gene or RNA molecule.

EQ GO:0010467; gene expression

EX differential expression of TNMD; <nowiki>https://biomarkerkb.org/canonical/BB1486</nowiki>

//

Biomarker Modifications Terms Controlled Vocabulary

2025-12-10T15:53:05Z

DaniallMasood:

[[Biomarker Entity Types Controlled Vocabulary]]

[[Biomarker Reporting Terms Controlled Vocabulary]]

[[Biomarker Aspect Terms Controlled Vocabulary]]

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Modifications Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of molecular modifications representing

chemical or post-translational changes to biomarker entities.

Name: modifications.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists controlled modification terms that describe chemical,

structural, or enzymatic alterations to biomarker entities. These terms

are used to represent states such as methylation, phosphorylation, or

glycosylation, enabling consistent annotation of modified biomarker forms.

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Modification term Once; starts an entry

AC Accession Unique identifier (MO-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID methylation

AC MO-0001

DE The process in which a methyl group is covalently attached to a molecule.

HI chemical modification

EQ GO:0032259; methylation

EX presence of methylation in DCC; <nowiki>https://biomarkerkb.org/canonical/AN6479</nowiki>

//

ID phosphorylation

AC MO-0002

DE The process of introducing a phosphate group into a molecule, usually

DE with the formation of a phosphoric ester, a phosphoric anhydride or a

DE phosphoric amide.

HI post-translational modification

EQ GO:0016310; phosphorylation

EX AKT1 S473 Phosphorylation; <nowiki>https://biomarkerkb.org/canonical/AN4659</nowiki>

//

ID acetylation

AC MO-0003

DE Acetylation involves the covalent linkage of an acetyl group into an

DE organic molecule.

HI post-translational modification

EQ NCIT:C16255; Acetylation

//

ID glycosylation

AC MO-0004

DE The covalent chemical or biochemical addition of carbohydrate or glycosyl

DE groups to other chemicals, by glycosyl transferases.

HI post-translational modification

EQ NCIT:C21034; Glycosylation

//

Biomarker Aspect Terms Controlled Vocabulary

2025-12-10T15:52:20Z

DaniallMasood:

Biomarker Reporting Terms Controlled Vocabulary

2025-12-10T15:51:49Z

DaniallMasood:

[[Biomarker Entity Types Controlled Vocabulary]]

[[Biomarker Aspect Terms Controlled Vocabulary]]

[[Biomarker Modifications Terms Controlled Vocabulary]]

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Reporting Terms Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of standardized terms used to describe

biomarker status, behavior, or detection in curated data entries.

Name: measured.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists the controlled reporting terms used to describe

biomarkers in the BiomarkerKB curation system. These nouns are used

in fields like `biomarker` to standardize natural language descriptions.

These terms are classified into categories such as:

''' * increased'''

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Reporting term Once; starts an entry

AC Accession Unique identifier (RT-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID increased

AC RT-0001

DE Indicates an assessed biomarker entity level is higher than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002300; increased quality

EX increased IL6 level; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID decreased

AC RT-0002

DE Indicates an assessed biomarker entity level is lower than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002301; decreased quality

EX decreased albumin level; <nowiki>https://biomarkerkb.org/canonical/AN6351</nowiki>

//

ID presence of

AC RT-0006

DE Indicates that the assessed entity is present.

HI presence

EX presence of rs180177132 mutation in PALB2;

EX <nowiki>https://biomarkerkb.org/canonical/AV9568</nowiki>

//

ID difference

AC RT-0007

DE A statistic that is a subtraction of one quantity from another.

HI expression

EQ STATO:0000613; difference

EX differential expression of TNMD; <nowiki>https://biomarkerkb.org/canonical/BB1486</nowiki>

//

Biomarker Entity Types Controlled Vocabulary

2025-12-10T15:51:06Z

DaniallMasood:

[[Biomarker Reporting Terms Controlled Vocabulary]]

[[Biomarker Aspect Terms Controlled Vocabulary]]

[[Biomarker Modifications Terms Controlled Vocabulary]]

<nowiki>--------------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary

Biomarker Entity Types Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>--------------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of biomarker entity types used in BiomarkerKB

Name: biomarker_entity_types.txt

Release: 2025_07_16

<nowiki>--------------------------------------------------------------------------------</nowiki>

This document lists the biomarker entity types used in the BiomarkerKB

curation system. When a biomarker entity is paired with terms from

measurable.txt (increase, decrease, presence, absence, etc.), they form the

biomarker. The biomarker entity types are:

''' * sequence variation'''

''' * protein'''

''' * metabolite'''

''' * glycan'''

''' * DNA'''

''' * RNA'''

''' * cell'''

''' * lipid'''

''' * image'''

Each entry consists of the following line codes:

---- ---------- --------------------------- -------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- -------------------------------

ID Identifier Biomarker entity name Once; starts an entry

AC Accession Unique identifier (BM-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

UF Used For Instructions for use Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example A biomarker that uses Optional; zero or once; line

the indicated entity wrapping possible

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>--------------------------------------------------------------------------------</nowiki>

ID sequence variation

AC BM-0001

DE For polymers, any difference between the observed order of monomer units

DE within a polymer chain and the order within a suitably comparable reference

DE polymer.

UF Entities that differ when specifically comparing sequence; includes

UF polymorphisms and mutations of genetic material and proteins.

HI sequence variation

EQ SO:0001059; sequence_alteration

EX presence of rs11571833 mutation in BRCA2;

EX https://biomarkerkb.org/biomarker/AN7363-3

//

ID protein

AC BM-0002

DE Amino acid chain formed by ribosome-mediated translation of a genetically-

DE encoded mRNA, and any post-translationally modified derivatives.

UF Entities that are composed, in whole or in part, of protein; includes

UF protein complexes and mixed-composition entities such as glycoproteins.

HI protein

EQ MESH:D011506; Proteins

EQ PR:000000001; protein

EX Interleukin-6; https://biomarkerkb.org/canonical/AN6278

NT Mixed-composition entities can get multiple entity-type tags; for example,a

NT glycoprotein would be both ‘protein’ and ‘glycan’.

//

ID metabolite

AC BM-0003

DE A small molecule involved in metabolism.

UF Entities that are inputs to or outputs from metabolic reactions.

HI metabolite

EQ CHEBI:25212; metabolite

EX UREA; https://biomarkerkb.org/canonical/AN6341

NT '' MESH term for metabolite being requested.''

//

ID glycan

AC BM-0004

DE Any oligosaccharide, polysaccharide or their derivatives consisting of

DE monosaccharides or monosaccharide derivatives linked by glycosidic bonds.

UF Entities that are composed, in whole or in part, of glycan; includes

UF protein complexes and mixed-composition entities such as proteoglycans.

HI glycan

EQ CHEBI:167559

EX N-glycan; <nowiki>https://biomarkerkb.org/canonical/AN6729</nowiki>

NT Mixed-composition entities can get multiple entity-type tags; for example,a

NT proteoglycan would be both ‘protein’ and ‘glycan’.

//

ID DNA

AC BM-0005

DE A polymer of deoxyribose-containing nucleotides linked by phosphodiester

DE bonds.

UF Entities that are composed of DNA when measured in bulk, or that are direct

UF or proxy measures of gene expression.

HI DNA

EQ MESH:D004247; DNA

EX cfDNA; https://biomarkerkb.org/canonical/AN6380

//

ID RNA

AC BM-00096

DE A polymer of ribose-containing nucleotides linked by phosphodiester bonds.

UF Entities that are composed of RNA when measured in bulk, or that are direct

UF or proxy measures of RNA expression.

HI RNA

EQ MESH:D012313; RNA

EX miRNA-21; https://biomarkerkb.org/canonical/AN6498

//

ID cell

AC BM-0007

DE An organism (or part thereof) that is a maximally connected compartment

DE surrounded by a plasma membrane.

UF Entities that are cells; includes cases when ratios between cell types are

UF measured.

HI cell

EQ MESH:D002477; Cells

EQ CL:0000000; cell

EX WBC; https://biomarkerkb.org/canonical/AN6280

//

ID lipid

AC BM-0008

DE A class of biomolecules including fats, oils, and certain hormones.

UF Entities that are lipids.

HI lipid

EQ MESH:D008055; Lipids

EQ CHEBI:18059; lipid

EX very long chain fatty acid; <nowiki>https://biomarkerkb.org/canonical/AN6187</nowiki>

//

ID image

AC BM-0018

DE Any visual display of structural or functional patterns of organs or tissues for

DE clinical evaluation.

UF Entities evaluated via the use of an image.

HI image

EQ NCIT:C48179; Image

EX presence of ground-glass opacity;

EX https://data.oncomx.org/allbiomarkers/biomarker/A0076

NT For type ‘image’, the entity that is being imaged would be indicated as the

NT sample source.

//

Biomarker Modifications Terms Controlled Vocabulary

2025-11-06T19:28:37Z

DaniallMasood: new modifications controlled vocab page

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Modifications Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of molecular modifications representing

chemical or post-translational changes to biomarker entities.

Name: modifications.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists controlled modification terms that describe chemical,

structural, or enzymatic alterations to biomarker entities. These terms

are used to represent states such as methylation, phosphorylation, or

glycosylation, enabling consistent annotation of modified biomarker forms.

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Modification term Once; starts an entry

AC Accession Unique identifier (MO-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID methylation

AC MO-0001

DE The process in which a methyl group is covalently attached to a molecule.

HI chemical modification

EQ GO:0032259; methylation

EX presence of methylation in DCC; <nowiki>https://biomarkerkb.org/canonical/AN6479</nowiki>

//

ID phosphorylation

AC MO-0002

DE The process of introducing a phosphate group into a molecule, usually

DE with the formation of a phosphoric ester, a phosphoric anhydride or a

DE phosphoric amide.

HI post-translational modification

EQ GO:0016310; phosphorylation

EX AKT1 S473 Phosphorylation; <nowiki>https://biomarkerkb.org/canonical/AN4659</nowiki>

//

ID acetylation

AC MO-0003

DE Acetylation involves the covalent linkage of an acetyl group into an

DE organic molecule.

HI post-translational modification

EQ NCIT:C16255; Acetylation

//

ID glycosylation

AC MO-0004

DE The covalent chemical or biochemical addition of carbohydrate or glycosyl

DE groups to other chemicals, by glycosyl transferases.

HI post-translational modification

EQ NCIT:C21034; Glycosylation

//

Biomarker Aspect Terms Controlled Vocabulary

2025-11-06T19:26:41Z

DaniallMasood: made aspect controlled vocab page

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Aspect Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of standardized aspects used to describe

the specific measurable or biological feature of a biomarker entity.

Name: aspect.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists controlled terms that represent measurable or

definable aspects of a biomarker entity. These terms indicate what

property, modification, or characteristic of the biomarker is being

evaluated, enabling structured representation of natural language

phrases such as "increased IL6 level" or "presence of BRCA2 mutation".

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Aspect term Once; starts an entry

AC Accession Unique identifier (AS-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID level

AC AS-0001

DE A position on a scale measuring intensity, quality, or amount.

EQ NCIT:C25554; Level

EX increased IL6 level; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID expression

AC AS-0002

DE Production level or transcriptional activity of a gene or RNA molecule.

EQ GO:0010467; gene expression

EX differential expression of TNMD; <nowiki>https://biomarkerkb.org/canonical/BB1486</nowiki>

//

ID mutation

AC AS-0003

DE A change in a sequence or structure in comparison to a reference entity

DE due to an insertion, deletion or substitution event.

DE within a biomarker entity.

EQ MI:0118; mutation

EX presence of rs11571833 mutation in BRCA2;

EX <nowiki>https://biomarkerkb.org/biomarker/AN7363-3</nowiki>

//

BiomarkerKB Resource Integration

2025-11-06T17:42:46Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB, https://glycanage.com/publications, [https://www.cancergenomeinterpreter.org/biomarkers https://www.c], [https://github.com/issues/assigned?issue=clinical-biomarkers%7Cbiomarker-issue-repo%7C248 Glycan Biomarkers] ([https://github.com/glygener/CarboCurator code]), [https://www.alliancegenome.org/ Alliance Genome]

Please contact us at mazumder_lab@gwu.edu and daniallmasood@gwu.edu if you have any other resources that may contain biomarker data

=CIViC=
Status: Direct Integration into Data Model

* Clinical Interpretation of Variants in Cancer (CIViC).
* Provides cancer biomarkers in form of DNA mutations (dbSNPs).
* Platform provides clinicians treatment options for patients based on unique tumor profile.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=ClinVar=
Status: Direct Integration into Data Model

* Public archive of reports of human variations classified for diseases and drug responses.
* Provides biomarkers for all disease, but we have only curated cancer biomarkers for now.
** dbSNPs
** File is really big but will go back and use existing script to map all biomarkers from here into the data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=EDRN=
Status: Sample Integration into Data Model

* Cancer biomarkers.

=GWAS=
Status: Direct Integration into Data Model

* Published genome-wide association studies (GWAS).
* Provides biomarkers in form of SNPs.
* GWAS Catalog contains SNPs for a vast amount of diseases.
** Preliminary curation only focused on cancer.
** Will use existing script to map all biomarkers into data model.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations.
* Does not provide a change within the entity so we cannot collect biomarker data from here.
* However we can use it as a cross-reference within our cross-referencing section.
* Provides cross-reference to OMIM, SNOMED, and MONDO.

=LOINC=
Status: Cross-Reference

''Data provided by Metabolomics Workbench''

=MarkerDB=
Status: Direct Integration into Data Model

* Provides a lot of useful biomarker data and cross-references other resources as well.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc.
* Annotations that can be cross-referenced include the above.
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers.
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=Metabolomics Workbench=
Status: Direct Integration into Data Model

''Data provided by Metabolomics Workbench''

* Metabolite biomarkers utilized in the uniform newborn screening program.
* Detect treatable disorders that are life threatening or having long-term morbidity, before they become symptomatic.

=OncoKB=
Status: Cross-Reference

* Provides useful information on drugs and therapy options for different biomarker entities.
* Also provides information based on what condition the entity is related to.
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross-reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution.

=OncoMX=
Status: Direct Integration into Data Model

* integrated cancer mutation and expression resource for exploring cancer biomarkers
* Manual curation effort by GWU and JPL
* Over 600 single and panel biomarkers
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=OpenTargets=
Status: Direct Integration into Data Model

* Collects potential drug targets and therapeutic targets.
* Some effort was required to find the correct biomarker data.
* 1200 biomarkers collected.
** dbSNPs related to cancer and other disease
* License: Creative Commons Attribution-NonCommercial 4.0 International License.

=PubMed Central Biomarker Gene Set Curation=
Status: Direct Integration into Data Model

''Data provided by Avi Ma'ayan's LINCS group''

* This data set was created through manual curation of biomarker gene sets on Pubmed Central using the results of gene sets returned from Rummagene.
* Using the outputted search results within the Rummagene web server, we manually identified publications that associated different conditions and environmental exposures to biomarker gene sets.
* The biomarker gene sets were retrieved through the validation of the gene mentioned within each of the publications.
* The primary use case for this data is to identify biomarker panels/ gene sets associated with conditions.

=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data.
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted.
* Contextual information can be imputed if necessary.
* In UniProt there are found_in and entries that are actual biomarkers:
** found_in will get a cross-reference;
** actual biomarkers will be directly integrated.
* Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file.
* License is Creative Commons Attribution 4.0 International (CC BY 4.0).
=MetaKB=

Status: Direct Integration into Data Model

* Provides harmonized associations between cancer genomic variants, diseases, and therapeutic evidence.
* Aggregates and standardizes variant interpretation data from six major knowledgebases:
** CIViC (Clinical Interpretation of Variants in Cancer) [Already Integrated Directly]
** OncoKB [Yet to be integrated]
** JAX-CKB (The Jackson Laboratory Clinical Knowledgebase) [Yet to be integrated]
** MolecularMatch [Yet to be integrated]
** PMKB (Precision Medicine Knowledgebase) [Yet to be integrated]
** Cancer Genome Interpreter (CGI) – through its ''Cancer Biomarkers Database'' component .[Integrated]
* Enables mapping of variant–disease–drug relationships with supporting evidence levels, citations, and ontology alignment (e.g., genes, variants, diseases, and drugs).
* Data integration requires review to ensure harmonized entity mappings consistent with the BiomarkerKB data model.
* Focused on somatic variant–based biomarkers; contextual attributes such as tissue type, therapy response, or evidence type can be inferred or imputed where not directly specified.
* Manual curation may be required for entries with incomplete evidence annotation or lacking standard ontology references.
* Integration approach: direct mapping of variant, condition, and evidence entities; cross-references retained to original data sources.
* License: Aggregated data are available for non-commercial, research use only, respecting constituent licenses:
** CIViC – CC0 (Public Domain)
** PMKB – CC-BY 4.0
** CGI – CC0 for biomarkers database, CC-BY-NC 4.0 for tool
** JAX-CKB – CC-BY-NC-SA 4.0
** OncoKB – custom non-commercial license
** MolecularMatch – restricted commercial use
** MetaKB codebase – MIT license
* Overall usage requires adherence to non-commercial research terms; commercial use needs separate permissions from individual data providers.

Biomarker Reporting Terms Controlled Vocabulary

2025-10-08T19:47:16Z

DaniallMasood: Created page with "<nowiki>---------------------------------------------------------------------------</nowiki> BiomarkerKB Controlled Vocabulary: Biomarker Reporting Terms Controlled Vocabulary File Biomarker Knowledgebase Project <nowiki>---------------------------------------------------------------------------</nowiki> Description: Controlled vocabulary of standardized terms used to describe biomarker status, behavior, or detection in curate..."

<nowiki>---------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary:

Biomarker Reporting Terms Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of standardized terms used to describe

biomarker status, behavior, or detection in curated data entries.

Name: measured.txt

Release: 2025_07_16

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists the controlled reporting terms used to describe

biomarkers in the BiomarkerKB curation system. These nouns are used

in fields like `biomarker` to standardize natural language descriptions.

These terms are classified into categories such as:

''' * increased'''

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Reporting term Once; starts an entry

AC Accession Unique identifier (RT-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID increased

AC RT-0001

DE Indicates an assessed biomarker entity level is higher than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002300; increased quality

EX increased IL6 level; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID decreased

AC RT-0002

DE Indicates an assessed biomarker entity level is lower than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002301; decreased quality

EX decreased albumin level; <nowiki>https://biomarkerkb.org/canonical/AN6351</nowiki>

//

ID presence of

AC RT-0006

DE Indicates that the assessed entity is present.

HI presence

EX presence of rs180177132 mutation in PALB2;

EX <nowiki>https://biomarkerkb.org/canonical/AV9568</nowiki>

//

ID difference

AC RT-0007

DE A statistic that is a subtraction of one quantity from another.

HI expression

EQ STATO:0000613; difference

EX differential expression of TNMD; <nowiki>https://biomarkerkb.org/canonical/BB1486</nowiki>

//

Biomarker Entity Types Controlled Vocabulary

2025-10-08T19:46:26Z

DaniallMasood: Created page with "<nowiki>--------------------------------------------------------------------------------</nowiki> BiomarkerKB Controlled Vocabulary Biomarker Entity Types Controlled Vocabulary File Biomarker Knowledgebase Project <nowiki>--------------------------------------------------------------------------------</nowiki> Description: Controlled vocabulary of biomarker entity types used in BiomarkerKB Name: biomarker_entity_types.txt Release: 2025_07_16 <now..."

<nowiki>--------------------------------------------------------------------------------</nowiki>

BiomarkerKB Controlled Vocabulary

Biomarker Entity Types Controlled Vocabulary File

Biomarker Knowledgebase Project

<nowiki>--------------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of biomarker entity types used in BiomarkerKB

Name: biomarker_entity_types.txt

Release: 2025_07_16

<nowiki>--------------------------------------------------------------------------------</nowiki>

This document lists the biomarker entity types used in the BiomarkerKB

curation system. When a biomarker entity is paired with terms from

measurable.txt (increase, decrease, presence, absence, etc.), they form the

biomarker. The biomarker entity types are:

''' * sequence variation'''

''' * protein'''

''' * metabolite'''

''' * glycan'''

''' * DNA'''

''' * RNA'''

''' * cell'''

''' * lipid'''

''' * image'''

Each entry consists of the following line codes:

---- ---------- --------------------------- -------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- -------------------------------

ID Identifier Biomarker entity name Once; starts an entry

AC Accession Unique identifier (BM-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

UF Used For Instructions for use Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example A biomarker that uses Optional; zero or once; line

the indicated entity wrapping possible

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>--------------------------------------------------------------------------------</nowiki>

ID sequence variation

AC BM-0001

DE For polymers, any difference between the observed order of monomer units

DE within a polymer chain and the order within a suitably comparable reference

DE polymer.

UF Entities that differ when specifically comparing sequence; includes

UF polymorphisms and mutations of genetic material and proteins.

HI sequence variation

EQ SO:0001059; sequence_alteration

EX presence of rs11571833 mutation in BRCA2;

EX https://biomarkerkb.org/biomarker/AN7363-3

//

ID protein

AC BM-0002

DE Amino acid chain formed by ribosome-mediated translation of a genetically-

DE encoded mRNA, and any post-translationally modified derivatives.

UF Entities that are composed, in whole or in part, of protein; includes

UF protein complexes and mixed-composition entities such as glycoproteins.

HI protein

EQ MESH:D011506; Proteins

EQ PR:000000001; protein

EX Interleukin-6; https://biomarkerkb.org/canonical/AN6278

NT Mixed-composition entities can get multiple entity-type tags; for example,a

NT glycoprotein would be both ‘protein’ and ‘glycan’.

//

ID metabolite

AC BM-0003

DE A small molecule involved in metabolism.

UF Entities that are inputs to or outputs from metabolic reactions.

HI metabolite

EQ CHEBI:25212; metabolite

EX UREA; https://biomarkerkb.org/canonical/AN6341

NT '' MESH term for metabolite being requested.''

//

ID glycan

AC BM-0004

DE Any oligosaccharide, polysaccharide or their derivatives consisting of

DE monosaccharides or monosaccharide derivatives linked by glycosidic bonds.

UF Entities that are composed, in whole or in part, of glycan; includes

UF protein complexes and mixed-composition entities such as proteoglycans.

HI glycan

EQ CHEBI:167559

EX N-glycan; <nowiki>https://biomarkerkb.org/canonical/AN6729</nowiki>

NT Mixed-composition entities can get multiple entity-type tags; for example,a

NT proteoglycan would be both ‘protein’ and ‘glycan’.

//

ID DNA

AC BM-0005

DE A polymer of deoxyribose-containing nucleotides linked by phosphodiester

DE bonds.

UF Entities that are composed of DNA when measured in bulk, or that are direct

UF or proxy measures of gene expression.

HI DNA

EQ MESH:D004247; DNA

EX cfDNA; https://biomarkerkb.org/canonical/AN6380

//

ID RNA

AC BM-00096

DE A polymer of ribose-containing nucleotides linked by phosphodiester bonds.

UF Entities that are composed of RNA when measured in bulk, or that are direct

UF or proxy measures of RNA expression.

HI RNA

EQ MESH:D012313; RNA

EX miRNA-21; https://biomarkerkb.org/canonical/AN6498

//

ID cell

AC BM-0007

DE An organism (or part thereof) that is a maximally connected compartment

DE surrounded by a plasma membrane.

UF Entities that are cells; includes cases when ratios between cell types are

UF measured.

HI cell

EQ MESH:D002477; Cells

EQ CL:0000000; cell

EX WBC; https://biomarkerkb.org/canonical/AN6280

//

ID lipid

AC BM-0008

DE A class of biomolecules including fats, oils, and certain hormones.

UF Entities that are lipids.

HI lipid

EQ MESH:D008055; Lipids

EQ CHEBI:18059; lipid

EX very long chain fatty acid; <nowiki>https://biomarkerkb.org/canonical/AN6187</nowiki>

//

ID image

AC BM-0018

DE Any visual display of structural or functional patterns of organs or tissues for

DE clinical evaluation.

UF Entities evaluated via the use of an image.

HI image

EQ NCIT:C48179; Image

EX presence of ground-glass opacity;

EX https://data.oncomx.org/allbiomarkers/biomarker/A0076

NT For type ‘image’, the entity that is being imaged would be indicated as the

NT sample source.

//

Controlled Vocabulary and Keywords

2025-09-15T16:52:00Z

DaniallMasood: New page for keywords

<nowiki>---------------------------------------------------------------------------</nowiki>

== BiomarkerKB Controlled Vocabulary ==
Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

=== Biomarker Entity Types Controlled Vocabulary File ===
Description: Controlled vocabulary of biomarker entity types used in BiomarkerKB.

Name: biomarker_entity_types.txt

Release: 2025_09_02

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists the biomarker entity types used in the BiomarkerKB

curation system. When a biomarker entity is paired with measurable.txt terms (increase, decrease, presence, absence etc.), they form the biomarker. The biomarker entity types are:

''' * gene'''

''' * protein'''

''' * metabolite'''

''' * glycan'''

''' * DNA'''

''' * RNA'''

''' * cell'''

''' * lipid'''

''' * image'''

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Biomarker entity name Once; starts an entry

AC Accession Unique identifier (BM-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example A biomarker that uses Optional; zero or once; line

the indicated entity wrapping possible

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID sequence

AC BM-0001

DE Nucleic acid sequences that function as units of heredity and which code for

DE the basic instructions for the development, reproduction, and maintenance

DE of an organism.

HI sequence

EQ MESH:D008969; Molecular Sequence Data

EQ SO:0000704; gene

EX BRCA1; <nowiki>https://biomarkerkb.org/canonical/AN4824</nowiki>

//

ID protein

AC BM-0002

DE Amino acid chain formed by ribosome-mediated translation of a genetically-

DE encoded mRNA, and any post-translationally modified derivatives.

HI protein

EQ MESH:D011506; Proteins

EQ PR:000000001; protein

EX Interleukin-6; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID metabolite

AC BM-0003

DE A small molecule involved in metabolism.

HI metabolite

EQ CHEBI:25212; metabolite

EX UREA; <nowiki>https://biomarkerkb.org/canonical/AN6341</nowiki>

NT '' MESH term for metabolite being requested.''

//

ID glycan

AC BM-0004

DE Any oligosaccharide, polysaccharide or their derivatives consisting of

DE monosaccharides or monosaccharide derivatives linked by glycosidic bonds.

HI glycan

EQ CHEBI:167559

EX N-glycan; <nowiki>https://biomarkerkb.org/canonical/AN6729</nowiki>

//

ID DNA

AC BM-0005

DE A polymer of deoxyribose-containing nucleotides linked by phosphodiester bonds.

HI DNA

EQ MESH:D004247; DNA

EX cfDNA; <nowiki>https://biomarkerkb.org/canonical/AN6380</nowiki>

//

ID RNA

AC BM-00096

DE A polymer of ribose-containing nucleotides linked by phosphodiester bonds.

HI RNA

EQ MESH:D012313; RNA

EX miRNA-21; <nowiki>https://biomarkerkb.org/canonical/AN6498</nowiki>

//

ID cell

AC BM-0007

DE An organism (or part thereof) that is a maximally connected compartment

DE surrounded by a plasma membrane.

HI cell

EQ MESH:D002477; Cells

EQ CL:0000000; cell

EX WBC; <nowiki>https://biomarkerkb.org/canonical/AN6280</nowiki>

//

ID lipid

AC BM-0008

DE A class of biomolecules including fats, oils, and certain hormones.

HI lipid

EQ MESH:D008055; Lipids

EQ CHEBI:18059; lipid

EX very long chain fatty acid; <nowiki>https://biomarkerkb.org/canonical/AN6187</nowiki>

//

ID image

AC BM-0018

DE Any visual display of structural or functional patterns of organs or tissues for

DE clinical evaluation.

HI image

EQ NCIT:C48179; Image

EX presence of ground-glass opacity;

EX <nowiki>https://data.oncomx.org/allbiomarkers/biomarker/A0076</nowiki>

//

=== Biomarker Reporting Terms Controlled Vocabulary File ===
Biomarker Knowledgebase Project

<nowiki>---------------------------------------------------------------------------</nowiki>

Description: Controlled vocabulary of standardized terms used to describe

biomarker status, behavior, or detection in curated data entries.

Name: measured.txt

Release: 2025_09_09

<nowiki>---------------------------------------------------------------------------</nowiki>

This document lists the controlled reporting terms used to describe

biomarkers in the BiomarkerKB curation system. These nouns are used

in fields like `biomarker` to standardize natural language descriptions and

can be modified using adjectives (“significant”, “gradual”, etc).

These terms are classified into categories such as:

''' * increased'''

Each entry consists of the following line codes:

---- ---------- --------------------------- ---------------------------------

Code Name Content Occurrences

---- ---------- --------------------------- ---------------------------------

ID Identifier Reporting term Once; starts an entry

AC Accession Unique identifier (RT-xxxx) Once

DE Definition Definition/description Once; line wrapping possible

HI Hierarchy Optional; zero or more

SY Synonym Optional; zero or more

EQ Equivalent External identifier with Optional; zero or more

same meaning

EX Example Optional; zero or once

NT Notes Optional; zero or once; line

wrapping possible

// Terminator End of entry Once; ends an entry

<nowiki>---------------------------------------------------------------------------</nowiki>

ID increased

AC RT-0001

DE Indicates an assessed biomarker entity level is higher than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002300; increased quality

EX increased IL6 level; <nowiki>https://biomarkerkb.org/canonical/AN6278</nowiki>

//

ID decreased

AC RT-0002

DE Indicates an assessed biomarker entity level is lower than normal by

DE a clinically relevant degree.

HI abundance

EQ PATO:0002301; decreased quality

EX decreased albumin level; <nowiki>https://biomarkerkb.org/canonical/AN6351</nowiki>

//

ID presence of

AC RT-0006

DE Indicates that the assessed entity is present.

HI presence

EX presence of rs180177132 mutation in PALB2;

EX <nowiki>https://biomarkerkb.org/canonical/AV9568</nowiki>

//

ID difference

AC RT-0007

DE A statistic that is a subtraction of one quantity from another.

HI expression

EQ STATO:0000613; difference

EX differential expression of TNMD; <nowiki>https://biomarkerkb.org/canonical/BB1486</nowiki>

//

Data Submission/Data Upload

2025-09-04T19:10:32Z

DaniallMasood: /* assessed_biomarker_entity */

==Instructions to submit Biomarker Data==
To submit data for the BiomarkerKB Portal, the biomarker data model must be followed. Instructions on how to format the data for submission, where to send it, and creating a BCO for the data submitted will be provided below.

# Biomarker data collected should follow the biomarker data model.
# "Core" fields should be filled in from the data source where biomarker data is collected. Core fields:
## biomarker
## assessed_biomarker_entity and assessed_biomarker_entity_id
## condition and condition_id OR exposure_agent and exposure_agent_id
# Other fields and annotations may also be collected from the data source, however if data is missing it can also be inferred or mapped from other sources.
# Apply the following standards to the data when possible:
## condition_id = DOID
## specimen_id = UBERON
## evidence_source = "SOURCE":"ID"
## For assessed_biomarker_entity_id please refer to this GitHub documentation for which standards to follow
# Provide extra annotations from your DCC/data with the agreed upon standards from the Biomarker Annotation RFC. This data does not have to follow the data model and can be submitted in a separate file.
## For example: Relevant EHR data/LOINC data for biomarkers/biomarker entities can be included in a separate sheet.
# Create a tsv/json file with the agreed upon fields which correspond to the biomarker data model. The data dictionary provides details on what the different fields represent.
## The preferred method for data submission is a json file as it will help ingest the data into the existing data efficiently. However, tsv file submissions are ok as well. In the GitHub, data_conversion.py script exists in the Data Conversion Folder and it will handle tsv to json file conversion and json to tsv file conversion as well.
## The biomarker data page has examples of tsv data submissions and how the data should be formatted with the appropriate biomarker fields. Example
# For panel biomarkers, if the biomarkers are part of the same panel, the biomarker_id value for each biomarker should be any string value that can uniquely identify which rows are part of the same biomarker panel. Documentation
# If curating data in tsv format: If biomarker rows are part of the same biomarker entry but differ on specimen, evidence, or role, then the biomarker_id for each row should be any string value that can uniquely identify which rows are part of the same biomarker.

=== Once data is formatted and cleaned please send any data to daniallmasood@gwu.edu ===
# Concurrently with submitting data please fill out the BCO Information: Biomarker Data Google Form.
## This will give metadata and description on how biomarker data was collected and is important for adding submitted data to the Biomarker Data page. An example of a previous BCO is provided in the sheet and available on the biomarker data page as well. [https://hivelab.biochemistry.gwu.edu/biomarker-partnership/data/BCO_000435 Example]
# If there are any further questions please consult the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for contributing data or reach out to Daniall using the email above.

==Standardized and Controlled Vocabulary==
There is a standard way to report some biomarker data. This section covers how the actual biomarker should be reported and how other fields should be filled out.

=== Condition ===
Condition should be reported in all lowercase and condition ID (from Disease Ontology ID) should be provided in the following column

=== assessed_biomarker_entity ===
assessed_biomarker_entity is the entity in which the change is assessed.

Should start off with a capital letter but if it is just a gene then it should remain in all capitals (e.g Myosin-binding protein H-like or IL6).

If the entity type is anything but a gene the whole name should be typed out.

=== assessed_entity_type ===
Report in all lowercase.

=== assessed_biomarker_entity_id ===
Refer to the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for the correct resource.

=== best_biomarker_role ===
Report in all lowercase. Refer to the [https://www.ncbi.nlm.nih.gov/books/NBK326791/ BEST Resource] for the correct biomarker role.

=== specimen ===
Report in all lowercase and specimen_ID in the following column should be from UBERON.

=== biomarker ===
The biomarker field is the most important. There are several distinctions here and changes are made based on the entity being reported. The text should be in lowercase except when a gene name appears then it should remain all uppercase.

==== Cell Biomarker ====
Should be reported as either:

* '''increased *cell name* count'''
* '''decreased *cell name* count'''
* Example: increased WBC count

==== Chemical Element Biomarker ====
Should be reported as either:

* '''increased *chemical element* level'''
* '''decreased *chemical element* level'''
* Example: increased Na+ level

==== DNA/RNA Biomarker ====
Should be reported as either:

* '''increased *DNA/RNA* level'''
* '''decreased *DNA/RNA* level'''
* Example: increased cfDNA level

==== Gene Biomarker ====
If the entity is a gene then there are different ways to report the biomarker based on how the mutation is reported:

* Expression of gene:
** '''*gene symbol* overexpression'''
** '''*gene symbol* underexpression'''
** Example: EGFR overexpression
* Amplification of gene: '''*gene symbol* amplification'''
* Specific site mutation in the expressed protein that is caused by the gene: '''*gene symbol* *site mutation* mutation'''
** Example: BRAF V600E mutation
* SNPs: '''presence of *dbSNP ID* mutation in *gene symbol*'''
** Example: presence of rs180177132 mutation in PALB2

==== Glycan Biomarker ====
Should be reported as: '''increased *glycan* level'''

* Example: increased N-glycan level

==== Metabolite Biomarker ====
Should be reported as:

* '''increased *metabolite* level'''
* '''decreased *metabolite* level'''
* Example: increased UREA level

==== Protein Biomarker ====
Should be reported as either:

* '''increased *protein symbol* level'''
* '''decreased *protein symbol* level'''
* Example: increased IL6 level

For more examples please refer to the [https://data.biomarkerkb.org/ BiomarkerKB Data Page]

Data Submission/Data Upload

2025-06-25T19:28:52Z

DaniallMasood: /* assessed_biomarker_entity */

==Instructions to submit Biomarker Data==
To submit data for the BiomarkerKB Portal, the biomarker data model must be followed. Instructions on how to format the data for submission, where to send it, and creating a BCO for the data submitted will be provided below.

# Biomarker data collected should follow the biomarker data model.
# "Core" fields should be filled in from the data source where biomarker data is collected. Core fields:
## biomarker
## assessed_biomarker_entity and assessed_biomarker_entity_id
## condition and condition_id OR exposure_agent and exposure_agent_id
# Other fields and annotations may also be collected from the data source, however if data is missing it can also be inferred or mapped from other sources.
# Apply the following standards to the data when possible:
## condition_id = DOID
## specimen_id = UBERON
## evidence_source = "SOURCE":"ID"
## For assessed_biomarker_entity_id please refer to this GitHub documentation for which standards to follow
# Provide extra annotations from your DCC/data with the agreed upon standards from the Biomarker Annotation RFC. This data does not have to follow the data model and can be submitted in a separate file.
## For example: Relevant EHR data/LOINC data for biomarkers/biomarker entities can be included in a separate sheet.
# Create a tsv/json file with the agreed upon fields which correspond to the biomarker data model. The data dictionary provides details on what the different fields represent.
## The preferred method for data submission is a json file as it will help ingest the data into the existing data efficiently. However, tsv file submissions are ok as well. In the GitHub, data_conversion.py script exists in the Data Conversion Folder and it will handle tsv to json file conversion and json to tsv file conversion as well.
## The biomarker data page has examples of tsv data submissions and how the data should be formatted with the appropriate biomarker fields. Example
# For panel biomarkers, if the biomarkers are part of the same panel, the biomarker_id value for each biomarker should be any string value that can uniquely identify which rows are part of the same biomarker panel. Documentation
# If curating data in tsv format: If biomarker rows are part of the same biomarker entry but differ on specimen, evidence, or role, then the biomarker_id for each row should be any string value that can uniquely identify which rows are part of the same biomarker.

=== Once data is formatted and cleaned please send any data to daniallmasood@gwu.edu ===
# Concurrently with submitting data please fill out the BCO Information: Biomarker Data Google Form.
## This will give metadata and description on how biomarker data was collected and is important for adding submitted data to the Biomarker Data page. An example of a previous BCO is provided in the sheet and available on the biomarker data page as well. [https://hivelab.biochemistry.gwu.edu/biomarker-partnership/data/BCO_000435 Example]
# If there are any further questions please consult the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for contributing data or reach out to Daniall using the email above.

==Standardized and Controlled Vocabulary==
There is a standard way to report some biomarker data. This section covers how the actual biomarker should be reported and how other fields should be filled out.

=== Condition ===
Condition should be reported in all lowercase and condition ID (from Disease Ontology ID) should be provided in the following column

=== assessed_biomarker_entity ===
Should start off with a capital letter but if it is just a gene then it should remain in all capitals (e.g Myosin-binding protein H-like or IL6).

If the entity type is anything but a gene the whole name should be typed out.

=== assessed_entity_type ===
Report in all lowercase.

=== assessed_biomarker_entity_id ===
Refer to the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for the correct resource.

=== best_biomarker_role ===
Report in all lowercase. Refer to the [https://www.ncbi.nlm.nih.gov/books/NBK326791/ BEST Resource] for the correct biomarker role.

=== specimen ===
Report in all lowercase and specimen_ID in the following column should be from UBERON.

=== biomarker ===
The biomarker field is the most important. There are several distinctions here and changes are made based on the entity being reported. The text should be in lowercase except when a gene name appears then it should remain all uppercase.

==== Cell Biomarker ====
Should be reported as either:

* '''increased *cell name* count'''
* '''decreased *cell name* count'''
* Example: increased WBC count

==== Chemical Element Biomarker ====
Should be reported as either:

* '''increased *chemical element* level'''
* '''decreased *chemical element* level'''
* Example: increased Na+ level

==== DNA/RNA Biomarker ====
Should be reported as either:

* '''increased *DNA/RNA* level'''
* '''decreased *DNA/RNA* level'''
* Example: increased cfDNA level

==== Gene Biomarker ====
If the entity is a gene then there are different ways to report the biomarker based on how the mutation is reported:

* Expression of gene:
** '''*gene symbol* overexpression'''
** '''*gene symbol* underexpression'''
** Example: EGFR overexpression
* Amplification of gene: '''*gene symbol* amplification'''
* Specific site mutation in the expressed protein that is caused by the gene: '''*gene symbol* *site mutation* mutation'''
** Example: BRAF V600E mutation
* SNPs: '''presence of *dbSNP ID* mutation in *gene symbol*'''
** Example: presence of rs180177132 mutation in PALB2

==== Glycan Biomarker ====
Should be reported as: '''increased *glycan* level'''

* Example: increased N-glycan level

==== Metabolite Biomarker ====
Should be reported as:

* '''increased *metabolite* level'''
* '''decreased *metabolite* level'''
* Example: increased UREA level

==== Protein Biomarker ====
Should be reported as either:

* '''increased *protein symbol* level'''
* '''decreased *protein symbol* level'''
* Example: increased IL6 level

For more examples please refer to the [https://data.biomarkerkb.org/ BiomarkerKB Data Page]

Data Submission/Data Upload

2025-06-25T15:29:27Z

DaniallMasood: Added controlled vocab

==Instructions to submit Biomarker Data==
To submit data for the BiomarkerKB Portal, the biomarker data model must be followed. Instructions on how to format the data for submission, where to send it, and creating a BCO for the data submitted will be provided below.

# Biomarker data collected should follow the biomarker data model.
# "Core" fields should be filled in from the data source where biomarker data is collected. Core fields:
## biomarker
## assessed_biomarker_entity and assessed_biomarker_entity_id
## condition and condition_id OR exposure_agent and exposure_agent_id
# Other fields and annotations may also be collected from the data source, however if data is missing it can also be inferred or mapped from other sources.
# Apply the following standards to the data when possible:
## condition_id = DOID
## specimen_id = UBERON
## evidence_source = "SOURCE":"ID"
## For assessed_biomarker_entity_id please refer to this GitHub documentation for which standards to follow
# Provide extra annotations from your DCC/data with the agreed upon standards from the Biomarker Annotation RFC. This data does not have to follow the data model and can be submitted in a separate file.
## For example: Relevant EHR data/LOINC data for biomarkers/biomarker entities can be included in a separate sheet.
# Create a tsv/json file with the agreed upon fields which correspond to the biomarker data model. The data dictionary provides details on what the different fields represent.
## The preferred method for data submission is a json file as it will help ingest the data into the existing data efficiently. However, tsv file submissions are ok as well. In the GitHub, data_conversion.py script exists in the Data Conversion Folder and it will handle tsv to json file conversion and json to tsv file conversion as well.
## The biomarker data page has examples of tsv data submissions and how the data should be formatted with the appropriate biomarker fields. Example
# For panel biomarkers, if the biomarkers are part of the same panel, the biomarker_id value for each biomarker should be any string value that can uniquely identify which rows are part of the same biomarker panel. Documentation
# If curating data in tsv format: If biomarker rows are part of the same biomarker entry but differ on specimen, evidence, or role, then the biomarker_id for each row should be any string value that can uniquely identify which rows are part of the same biomarker.

=== Once data is formatted and cleaned please send any data to daniallmasood@gwu.edu ===
# Concurrently with submitting data please fill out the BCO Information: Biomarker Data Google Form.
## This will give metadata and description on how biomarker data was collected and is important for adding submitted data to the Biomarker Data page. An example of a previous BCO is provided in the sheet and available on the biomarker data page as well. [https://hivelab.biochemistry.gwu.edu/biomarker-partnership/data/BCO_000435 Example]
# If there are any further questions please consult the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for contributing data or reach out to Daniall using the email above.

==Standardized and Controlled Vocabulary==
There is a standard way to report some biomarker data. This section covers how the actual biomarker should be reported and how other fields should be filled out.

=== Condition ===
Condition should be reported in all lowercase and condition ID (from Disease Ontology ID) should be provided in the following column

=== assessed_biomarker_entity ===
Should start off with a capital letter but if it is just a gene then it should remain in all capitals (e.g Myosin-binding protein H-like or IL6)

=== assessed_entity_type ===
Report in all lowercase.

=== assessed_biomarker_entity_id ===
Refer to the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for the correct resource.

=== best_biomarker_role ===
Report in all lowercase. Refer to the [https://www.ncbi.nlm.nih.gov/books/NBK326791/ BEST Resource] for the correct biomarker role.

=== specimen ===
Report in all lowercase and specimen_ID in the following column should be from UBERON.

=== biomarker ===
The biomarker field is the most important. There are several distinctions here and changes are made based on the entity being reported. The text should be in lowercase except when a gene name appears then it should remain all uppercase.

==== Cell Biomarker ====
Should be reported as either:

* '''increased *cell name* count'''
* '''decreased *cell name* count'''
* Example: increased WBC count

==== Chemical Element Biomarker ====
Should be reported as either:

* '''increased *chemical element* level'''
* '''decreased *chemical element* level'''
* Example: increased Na+ level

==== DNA/RNA Biomarker ====
Should be reported as either:

* '''increased *DNA/RNA* level'''
* '''decreased *DNA/RNA* level'''
* Example: increased cfDNA level

==== Gene Biomarker ====
If the entity is a gene then there are different ways to report the biomarker based on how the mutation is reported:

* Expression of gene:
** '''*gene symbol* overexpression'''
** '''*gene symbol* underexpression'''
** Example: EGFR overexpression
* Amplification of gene: '''*gene symbol* amplification'''
* Specific site mutation in the expressed protein that is caused by the gene: '''*gene symbol* *site mutation* mutation'''
** Example: BRAF V600E mutation
* SNPs: '''presence of *dbSNP ID* mutation in *gene symbol*'''
** Example: presence of rs180177132 mutation in PALB2

==== Glycan Biomarker ====
Should be reported as: '''increased *glycan* level'''

* Example: increased N-glycan level

==== Metabolite Biomarker ====
Should be reported as:

* '''increased *metabolite* level'''
* '''decreased *metabolite* level'''
* Example: increased UREA level

==== Protein Biomarker ====
Should be reported as either:

* '''increased *protein symbol* level'''
* '''decreased *protein symbol* level'''
* Example: increased IL6 level

For more examples please refer to the [https://data.biomarkerkb.org/ BiomarkerKB Data Page]

Data Submission/Data Upload

2025-06-24T18:43:47Z

DaniallMasood:

==Instructions to submit Biomarker Data==
To submit data for the BiomarkerKB Portal, the biomarker data model must be followed. Instructions on how to format the data for submission, where to send it, and creating a BCO for the data submitted will be provided below.

# Biomarker data collected should follow the biomarker data model.
# "Core" fields should be filled in from the data source where biomarker data is collected. Core fields:
## biomarker
## assessed_biomarker_entity and assessed_biomarker_entity_id
## condition and condition_id OR exposure_agent and exposure_agent_id
# Other fields and annotations may also be collected from the data source, however if data is missing it can also be inferred or mapped from other sources.
# Apply the following standards to the data when possible:
## condition_id = DOID
## specimen_id = UBERON
## evidence_source = "SOURCE":"ID"
## For assessed_biomarker_entity_id please refer to this GitHub documentation for which standards to follow
# Provide extra annotations from your DCC/data with the agreed upon standards from the Biomarker Annotation RFC. This data does not have to follow the data model and can be submitted in a separate file.
## For example: Relevant EHR data/LOINC data for biomarkers/biomarker entities can be included in a separate sheet.
# Create a tsv/json file with the agreed upon fields which correspond to the biomarker data model. The data dictionary provides details on what the different fields represent.
## The preferred method for data submission is a json file as it will help ingest the data into the existing data efficiently. However, tsv file submissions are ok as well. In the GitHub, data_conversion.py script exists in the Data Conversion Folder and it will handle tsv to json file conversion and json to tsv file conversion as well.
## The biomarker data page has examples of tsv data submissions and how the data should be formatted with the appropriate biomarker fields. Example
# For panel biomarkers, if the biomarkers are part of the same panel, the biomarker_id value for each biomarker should be any string value that can uniquely identify which rows are part of the same biomarker panel. Documentation
# If curating data in tsv format: If biomarker rows are part of the same biomarker entry but differ on specimen, evidence, or role, then the biomarker_id for each row should be any string value that can uniquely identify which rows are part of the same biomarker.
# Once data is formatted and cleaned please send any data to daniallmasood@gwu.edu
# Concurrently with submitting data please fill out the BCO Information: Biomarker Data Google Form.
## This will give metadata and description on how biomarker data was collected and is important for adding submitted data to the Biomarker Data page. An example of a previous BCO is provided in the sheet and available on the biomarker data page as well. [https://hivelab.biochemistry.gwu.edu/biomarker-partnership/data/BCO_000435 Example]
# If there are any further questions please consult the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for contributing data or reach out to Daniall using the email above.

==Standardized Data Reporting==
There is a standard way to report some biomarker data. This section covers how the actual biomarker should be reported and how other fields should be filled out.

=== Condition ===
Condition should be reported in all lowercase and condition ID should be provided in the following column

=== assessed_biomarker_entity ===
Should start off with a capital letter but if it is just a gene then it should remain in all capitals (e.g Myosin-binding protein H-like or IL6)

=== Biomarker ===
The biomarker field is the most important. There are several distinctions here and changes are made based on the entity being reported. The text should be in lowercase except when a gene name appears then it should remain all uppercase.

* If the entity for the biomarker is a protein then the biomarker should be reported as "increased/decreased levels of *protein*"
** If reporting a mutation in a protein then "*protein symbol* *site mutation*"
* If the entity is a gene then there are different ways to report the biomarker based on how the mutation is reported:
** If reporting expression of gene then "*gene* overexpression/underexpression"
** If reporting a dbSNP then "presence of *dbSNPID* mutation in *gene*"
** If reporting a site change then "*gene name* *site mutation*
* Other entities such as metabolite, cell, RNA should be reported as protein biomarkers are reported

For more examples please refer to the [https://data.biomarkerkb.org/ BiomarkerKB Data Page]

Data Submission/Data Upload

2025-06-24T18:31:04Z

DaniallMasood:

Data Submission/Data Upload

2025-06-24T18:30:32Z

DaniallMasood:

Instructions to submit Biomarker Data
To submit data for the BiomarkerKB Portal, the biomarker data model must be followed. Instructions on how to format the data for submission, where to send it, and creating a BCO for the data submitted will be provided below.

# Biomarker data collected should follow the biomarker data model.
# "Core" fields should be filled in from the data source where biomarker data is collected. Core fields:
## biomarker
## assessed_biomarker_entity and assessed_biomarker_entity_id
## condition and condition_id OR exposure_agent and exposure_agent_id
# Other fields and annotations may also be collected from the data source, however if data is missing it can also be inferred or mapped from other sources.
# Apply the following standards to the data when possible:
## condition_id = DOID
## specimen_id = UBERON
## evidence_source = "SOURCE":"ID"
## For assessed_biomarker_entity_id please refer to this GitHub documentation for which standards to follow
# Provide extra annotations from your DCC/data with the agreed upon standards from the Biomarker Annotation RFC. This data does not have to follow the data model and can be submitted in a separate file.
## For example: Relevant EHR data/LOINC data for biomarkers/biomarker entities can be included in a separate sheet.
# Create a tsv/json file with the agreed upon fields which correspond to the biomarker data model. The data dictionary provides details on what the different fields represent.
## The preferred method for data submission is a json file as it will help ingest the data into the existing data efficiently. However, tsv file submissions are ok as well. In the GitHub, data_conversion.py script exists in the Data Conversion Folder and it will handle tsv to json file conversion and json to tsv file conversion as well.
## The biomarker data page has examples of tsv data submissions and how the data should be formatted with the appropriate biomarker fields. Example
# For panel biomarkers, if the biomarkers are part of the same panel, the biomarker_id value for each biomarker should be any string value that can uniquely identify which rows are part of the same biomarker panel. Documentation
# If curating data in tsv format: If biomarker rows are part of the same biomarker entry but differ on specimen, evidence, or role, then the biomarker_id for each row should be any string value that can uniquely identify which rows are part of the same biomarker.
# Once data is formatted and cleaned please send any data to daniallmasood@gwu.edu
# Concurrently with submitting data please fill out the BCO Information: Biomarker Data Google Form.
## This will give metadata and description on how biomarker data was collected and is important for adding submitted data to the Biomarker Data page. An example of a previous BCO is provided in the sheet and available on the biomarker data page as well. [https://hivelab.biochemistry.gwu.edu/biomarker-partnership/data/BCO_000435 Example]
# If there are any further questions please consult the [https://github.com/clinical-biomarkers/biomarker-partnership/blob/main/supplementary_files/documentation/contributing_data.md GitHub Documentation] for contributing data or reach out to Daniall using the email above.

Main Page

2025-06-24T16:44:12Z

DaniallMasood:

__NOTOC__
<div id="ggw-topbanner" style="clear:both; position:relative; box-sizing:border-box; width:100%; margin:1.2em 0 6px; min-width:47em; border:1px solid #ddd; background-color:#f9f9f9; color:#000;"><div style="margin:0.4em; text-align:center;"><div style="font-size:160%; padding:.1em;">Welcome to [https://biomarkerkb.org/ BiomarkerKB] Wiki</div>'''<u>What is BiomarkerKB?</u>'''
BiomarkerKB is a Common Fund Data Ecosystem (CFDE) sponsored project to develop a knowledgebase that will organize and integrate biomarker data from different public sources. The data will be connected to contextual information to show a novel systems-level view of biomarkers. The motivation for this project is to improve the harmonization and organization of biomarker data. This will be done by mapping biomarkers from public sources to, and across, CF data elements. This mapping will bridge knowledge across multiple Data Coordinating Centers (DCCs) and biomedical disciplines.

'''<u>Our Mission:</u>'''

Provide computational and informatics resources and tools for biomarker research.

Integrate biomarker data and knowledge from diverse resources.

'''<u>Our Goals:</u>'''

Mapping biomarkers from public sources to relevant data elements.

Mapping contextual data from participating CFDE DCCs.

Developing a framework for organizing biomarker data.

Creating tools for querying and exploring biomarker data.

Disseminating the results of this project to the research community.<div style="font-size:100%;"></div></div></div><div id="ggw_row2" style="display: flex; flex-flow: row wrap; justify-content: space-between; padding: 0; margin: 0 -5px 0 -5px;"><div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[BiomarkerKB Portal|BiomarkerKB Portal]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[BiomarkerKB Portal]]'' <br>
The [[BiomarkerKB portal]] is the web interface allowing browser based access to the Biomarker data...
</div>
</div>

<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[BiomarkerKB data repository|BiomarkerKB Data Repository]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[BiomarkerKB Data Repository]]'' <br>
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
=== [[Frequently Asked Questions|BiomarkerKB Web Services API]] ===
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[BiomarkerKB Web Services API]]'' <br>
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[Frequently Asked Questions]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[Frequently Asked Questions]]'' <br>
The FAQ section contains ...
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[Data Workflow & Data Model]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[Data Workflow & Data Model]]'' <br>

</div>
</div><div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[Data Submission/Data Upload]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[Data Submission/Data Upload]]'' <br>
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
<h3>[[Releases]]</h3>
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[Releases]]'' <br>
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
=== [[Biomarker About]] ===
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[Biomarker About]]'' <br>
</div>
</div>
<div style="flex: 1; margin: 5px; min-width: 210px; border: 1px solid #CCC; padding: 0 10px 10px 10px; box-shadow: 0 2px 2px rgba(0,0,0,0.1); background: #f5faff;">
=== [[BiomarkerKB Resource Integration]] ===
<div style="border-top: 1px solid #CCC; padding-top: 0.5em;">
    '''''Main article:''' [[BiomarkerKB Resource Integration]]'' <br>
</div>
</div>
</div>
</div>

BiomarkerKB Resource Integration

2025-06-10T13:56:28Z

DaniallMasood: Added previous resources and details

BiomarkerKB Resource Integration

2025-06-10T13:37:39Z

DaniallMasood:

BiomarkerKB Resource Integration

2025-05-20T13:26:21Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross-referenced include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated
*Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file

=HPO=

Status: Cross-Reference

* HPO provides disease and entity associations
* Does not provide a change within the entity
* So we cannot collect biomarker data from here
* However we can use it as a cross reference within our cross referencing section
* Provides cross reference to OMIM, SNOMED, and MONDO

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB

Please contact us at mazumder_lab@gwu.edu if you have any other

BiomarkerKB Resource Integration

2025-05-20T13:25:35Z

DaniallMasood: HPO added

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross-referenced include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated
*Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file

=HPO=

Status: Cross-Reference

HPO provides disease and entity associations
Does not provide a change within the entity
So we cannot collect biomarker data from here
However we can use it as a cross reference within our cross referencing section
Provides cross reference to OMIM, SNOMED, and MONDO

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB

Please contact us at mazumder_lab@gwu.edu if you have any other

Date Review Process and QC

2025-03-11T20:38:37Z

DaniallMasood: documented QC steps

As data is submitted and prepared it has to go through an internal review process by the BiomarkerKB team. Data is submitted from other collaborators and data contributing centers. Data is also collected internally by the BiomarkerKB team. Data can be collected through manual or automated curation. There are certain steps listed below on how the data is reviewed and QCed.

# If data is submitted from a collaborator or DCC, then the team at BiomarkerKB performs spot checks on the submitted data. This allows for a quick check to make sure that the data the is submitted is correct and in the correct biomarker data model format.
## Data from OncoMX was collected before the the BiomarkerKB project was started. The data was reformatted into the biomarker data model, but it went through 2 more rounds of QC to make sure the data was fully correct.
# The data is then run through the TSVtoJSON.py converter. This allows for the biomarker data to be run through different APIs to make sure the correct condition and entity IDs are being used. If some IDs are not recognized then a flag is created and the data can be corrected if needed.
# Once the above are corrected the biomarker data is run through another data_qc script. This script catches irregularities within the data such as casing mistakes, typos, formatting errors, and mismatch of biomarker IDs.
# Another last spot check is done on the data to ensure that all changes are corrected and the data has no other scientific issues.

Knowledge Graph

2025-03-11T15:45:00Z

DaniallMasood:

The biomarker knowledge graph will be used to identify novel relationships within biomarker data and annotations. Currently the biomarker knowledge graph is being built with current BiomarkerKB data alongside the CFDE data distillery knowledge graph. This allows for data from other data contributing centers to be utilized in finding novel relationships and novel information that will help the biomarker data and understanding of biomarkers. Biomarker relationships with treatments, drug targets and other entities can be explored as well through Cypher queries. These Cypher queries will be written to help explore relationships that are of interest biologically and have specific scientific use cases. Below is the recommended process of adding new biomarker data to the existing CFDE knowledge graph.

'''Recommended development process:'''
# Revise the edge and node files for BIOMARKER.
# ''Optional'': Upload the edge and node files to the Globus folder.
# Take a copy of the latest set of ontology CSVs of the Data Distillery minus the Biomarker data ('''DD-no-BIOMARKER''') and add it to your ETL environment.
# ''Add your new edge and node files to the folder that corresponds to the download folder of your Globus Connect Personal setup. Your copy of edges_nodes.ini should point to this folder. For example, I download everything from Globus to a subfolder of my Documents folder on my MacOs machine. My ini file looks like:''
## [Paths] # Local paths containing ingestion files ... BIOMARKER=/Users/jas971/documents/globus/Import/BIOMARKER

# Run the [https://github.com/x-atlas-consortia/ubkg-etl/tree/main/generation_framework ingestion script] to generate a new set of ontology CSVs with the new BIOMARKER ('''./build_csv.sh -v BIOMARKER'''), integrating your version of BIOMARKER with the '''DD-no-BIOMARKER'''.
# Using the ontology CSVs generated in step 5, execute the [https://github.com/x-atlas-consortia/ubkg-neo4j/blob/main/docs/BUILD_INSTRUCTIONS.md workflow] described in ubkg-neo4j to build a Docker container. As you've probably experienced, the longest waits are in the import of the CSVs and the time spent to create the relationship indexes. (Pro [or maybe jaded amateur] tip: if you find the import taking forever, especially for relationships, you're probably running into memory issues. Reboot and do over.)
''Acknowledgements: [https://dd-kg-ui.cfde.cloud/about Data Distillery Team at CFDE]''

Knowledge Graph

2025-03-11T14:54:53Z

DaniallMasood: added description

Knowledge Graph

2025-03-11T14:45:02Z

DaniallMasood: added knowledge graph wiki page

'''Recommended development process:'''
# Revise the edge and node files for BIOMARKER.
# ''Optional'': Upload the edge and node files to the Globus folder.
# Take a copy of the latest set of ontology CSVs of the Data Distillery minus the Biomarker data ('''DD-no-BIOMARKER''') and add it to your ETL environment.
# ''Add your new edge and node files to the folder that corresponds to the download folder of your Globus Connect Personal setup. Your copy of edges_nodes.ini should point to this folder. For example, I download everything from Globus to a subfolder of my Documents folder on my MacOs machine. My ini file looks like:''

# Run the ingestion script to generate a new set of ontology CSVs with the new BIOMARKER ('''./build_csv.sh -v BIOMARKER'''), integrating your version of BIOMARKER with the '''DD-no-BIOMARKER'''.
# Using the ontology CSVs generated in step 5, execute the workflow described in ubkg-neo4j to build a Docker container. As you've probably experienced, the longest waits are in the import of the CSVs and the time spent to create the relationship indexes. (Pro [or maybe jaded amateur] tip: if you find the import taking forever, especially for relationships, you're probably running into memory issues. Reboot and do over.)

BiomarkerKB Resource Integration

2025-03-11T14:42:51Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross-referenced include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated
*Manual curation of 56 reviewed entries with mention of "biomarker" in flat text file

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB

Please contact us at mazumder_lab@gwu.edu if you have any other

BiomarkerKB Resource Integration

2025-02-25T16:44:31Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB, SalivaDB

Please contact us at mazumder_lab@gwu.edu if you have any other

BiomarkerKB Resource Integration

2025-02-25T16:44:12Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

Please contact us at mazumder_lab@gwu.edu if you have any other

OncoMX

2025-01-28T17:00:29Z

DaniallMasood: Created page with "History Page for the OncoMX Cancer Biomarker Project"

History Page for the OncoMX Cancer Biomarker Project

BiomarkerKB Resource Integration

2025-01-28T16:59:24Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)
* In UniProt there are found_in and entries that are actual biomarkers
** found_in will get an cross reference
** actual biomarkers will be directly integrated

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

Please contact us at mazumder_lab@gwu.edu if you have any other

BiomarkerKB Resource Integration

2025-01-28T16:55:27Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be explored: [https://cadsr.cancer.gov/onedata/Home.jsp CADSR Cancer], https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

Please contact us at mazumder_lab@gwu.edu if you have any other

BiomarkerKB Resource Integration

2025-01-28T16:53:08Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be explored: CADSR Cancer, https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

BiomarkerKB Resource Integration

2025-01-28T16:51:59Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be added: CADSR Cancer, https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

BiomarkerKB Resource Integration

2025-01-28T16:49:28Z

DaniallMasood:

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources to be added: CADSR Cancer Center, https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

BiomarkerKB Resource Integration

2025-01-28T16:49:14Z

DaniallMasood: added resources

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

Other resources: CADSR Cancer Center, https://themarker.idrblab.cn/, biomarker.org, ResMarkerDB

BiomarkerKB Resource Integration

2025-01-24T20:31:12Z

DaniallMasood: Added UPKB, MarkerDB, OncoKB

BiomarkerKB collects data from many different resources. The data that is collected is not always directly integrated into the data model and data from a resource is sometimes just added as valuable contextual annotations or cross references.
=UniProtKB=

Status: Direct Integration into Data Model

* Can provide biomarker (change in entity), entity, condition, and sampling data
* This data is in a text file that has to be reviewed fully and to make sure it will be able to be automatically extracted
* Contextual information can be imputed if necessary
* License is Creative Commons Attribution 4.0 International (CC BY 4.0)

=MarkerDB=
Status: Cross Reference

* Provides a lot of useful biomarker data and cross-references other resources as well
* License: Creative Commons Attribution-NonCommercial 4.0 International License.
* Because of this license we can directly integrate data from MarkerDB into BiomarkerKB
* Information includes: panel information, abnormal levels of biomarkers by disease, structural information, etc
* Annotations that can be cross reference include the above
* By cross-referencing, BiomarkerKB will allow users to find more information for specific biomarkers and move towards the goal of being a comprehensive resource for biomarkers

=OncoKB=
Status: Cross reference

* Provides useful information on drugs and therapy options for different biomarker entities
* Also provides information based on what condition the entity is related to
* License: A license is required to use OncoKB for commercial and/or clinical purposes, and to access OncoKB data programmatically for academic purposes.
* Paid license is required
* Cross reference from biomarkers in BiomarkerKB to the appropriate drug information and therapy information is the best solution

BiomarkerKB Resource Integration

2025-01-23T21:34:37Z

DaniallMasood:

BiomarkerKB Resource Integration

2025-01-23T21:26:37Z

DaniallMasood:

BiomarkerKB Resource Integration

2025-01-23T20:57:45Z

DaniallMasood: Created new page for biomarker resource integration