Jorge L. Sepulveda

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal ( https://biomarkerkb.org ) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

Supplemental Table 1 Survival and Race Predictor Regulon Gene Descriptions & Regulons

Supplemental Figure 3 Racial difference in survival in (A) Luminal A compared to (B) TNBC breast cancer. CI, confidence interval.

Supplemental Figure 4 Quantitative correlation of biomarker expression stratified by race.

Supplemental Table 3 Race and Survival Predictor Regulon Gene Enrichment Ontology Processes

Supplemental Figure 5 Overall breast cancer survival based on median income of county of diagnosis in European American (EA) (A) compared to African American (AA) patients (B). CI, confidence interval.

Supplemental Table 2 Race and 3-year Survival Regulon RFS

Supplemental Figure 3 Racial difference in survival in (A) Luminal A compared to (B) TNBC breast cancer. CI, confidence interval.