Siva Sivaganesan

The paper develops a framework to predict signaling pathway activity changes by integrating genetic perturbation data across cell lines, improving prediction accuracy using multi-task learning and supervised methods.

ALOHA improves dose-response analysis by using Bayesian splines to model gene responses and cluster co-regulated genes, leading to better identification of biological pathways and benchmark doses.

The paper develops a method to predict the mechanism of action of cellular perturbations by analyzing pathway network topology and transcriptional signatures.