Vinay Srinivas Bharadhwaj

SynDRep predicts synergistic drug partners for disease repurposing using knowledge graphs and machine learning, achieving 84.58% true predictions with HolE and 0.796 ROC-AUC with random forest.

This study identifies shared molecular mechanisms between schizophrenia and somatic disorders using a computational framework based on multi-rank non-negative matrix factorization.

This study identifies shared genetic and biological mechanisms between schizophrenia, bipolar disorder, and type 2 diabetes mellitus using integrative analysis of gene expression datasets.

DecoPath is a web tool that helps interpret pathway enrichment analysis results by comparing across databases and methods, highlighting consensus and discrepancies.

CLEP generates patient representations by integrating prior biological knowledge with patient data, improving classification performance compared to using raw transcriptomics data.

This paper uses predictive machine learning models to simulate drug responses by calibrating patient-specific pathway signatures, identifying potential drugs and mechanisms for three cancers.