James P. Long

The paper introduces DSEN, a tissue-aware regression method that improves drug response prediction by decomposing models into shared and lineage-specific components, achieving better performance and interpretability.

This paper shows that combining IRE with anti-PD-1 induces abscopal effects in PDAC by triggering immune activation and systemic antitumor responses.

The paper shows that data leakage in drug response prediction models inflates accuracy and corrupts biomarker discovery, and provides a solution to avoid this issue.

SIR improves drug synergy testing by using shape-constrained regression and bootstrap methods to provide reliable statistical inference and error control.

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

The paper introduces SMAGS, a regression framework that maximizes sensitivity at a given specificity for binary classification, showing 14% improvement in sensitivity on a colorectal cancer dataset.