Vasant Honavar

MetaScore improves traditional protein-protein docking scoring functions using machine learning, outperforming existing methods in success and hit rates.

The paper introduces iScore, a software tool that ranks protein-protein docking models using a random walk graph kernel and support vector machines, with MPI and GPU support for efficient computation.

This study develops a network-based biomarker discovery framework for IBD using metagenomics data and shows it performs well, especially with small datasets.

iScore improves protein-protein docking model scoring by combining energy terms, graph representation, and evolutionary conservation, achieving top performance on benchmark datasets.

This study develops a multi-view feature selection method to predict ovarian cancer survival using multi-omics data and shows it outperforms single-view and baseline fusion approaches.