Sumana Srivatsa

This study evaluates perturbation response models and finds existing metrics are unreliable due to scale and dimensionality issues, and complex models underperform simple baselines.

This study develops a statistical framework to identify synthetic lethal gene pairs from cancer perturbation screens and validates novel interactions in hepatocellular carcinoma.

APSiC identifies novel cancer genes and non-genetic drivers from perturbation screens, revealing context-dependent oncogenic mechanisms and functional roles in specific cancers.

The paper improves pathway reconstruction from RNAi screens by incorporating off-target effects into nested effects models, enhancing network accuracy.