data-driven predictions of gene expression, function, regulation, and interactions in human
Data-driven
HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.
Genomics data compendium
Tissue-specific
HumanBase models tissue-specific gene interactions by leveraging experimentally verified tissue expression and gene function to learn from an immense data compendium of diverse tissues and cell-types. The resulting functional networks accurately capture tissue-specific gene function.
IL1B in mononuclear phagocyteNetwork-guided
Tissue-specific networks can guide genome-wide association (GWAS) analysis by effectively reprioritizing the associations from a GWAS study. With NetWAS (Network-guided GWAS Analysis), HumanBase can aide researchers in identifying additional disease-associated genes.
NetWAS analysis