Conrad Bessant

MORGaN: self-supervised multi-relational graph learning for drug target discovery

MORGaN is a self-supervised framework for drug target discovery that outperforms existing models by leveraging multi-omic, multi-relation gene networks and provides interpretable results.

Understanding the Sources of Performance in Deep Drug Response Models Reveals Insights and Improvements

The paper evaluates deep learning models for drug response prediction, finds transcriptomics data drives performance, and introduces new models that learn chemical features.

Community detection in empirical kinase networks identifies new members of signaling pathways

The paper applies community detection to kinase networks derived from phosphoproteomics data to identify new signaling pathway components, finding TTK as a potential member of PI3K/AKT/mTOR signaling.

GeoDiver: Differential Gene Expression Analysis & Gene-Set Analysis For GEO Datasets

GeoDiver provides an online tool for differential gene expression and gene-set analysis on GEO datasets, with interactive visualizations and results sharing capabilities.