Jessica C. Lal

TmSNet predicts tumor cell-specific total mRNA expression from multi-omic data, achieving high accuracy across cancer types and external cohorts.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

This study uses network-based methods to identify and validate metformin as a potential treatment for atrial fibrillation using human-derived atrial-like cardiomyocytes.

The paper identifies regions in protein sequences enriched for pathogenic variants using gene family alignments and demonstrates their enrichment in neurodevelopmental disorder de novo variants and ClinVar pathogenic classifications.