Joseph D. Romano

DRIVE-KG integrates multi-omics data into a knowledge graph to discover SNP-phenotype associations and predict endometriosis status, uncovering new variant-phenotype links and improving classification performance.

CASTER-DTA predicts drug-target affinity using equivariant graph neural networks, achieving state-of-the-art performance and generating a resource of binding affinities for all FDA-approved drugs against the human proteome.

The paper extends the TPOT AutoML tool to include covariate adjustments in biomedical data analysis, showing its utility in toxicogenomics and gene expression studies.

VenomKB v2.0 is an open-access resource for venom bioactivities, sequences, and classifications, providing tools for computational toxinology and drug discovery.