Prashant Gupta

The paper introduces kGain, a metric quantifying k-mer frequency shifts due to mutations, and shows it predicts adaptive potential across species and experimental systems.

Saturation-seq combines genome editing and single-cell RNA-seq to quantify variant effects on NFE2L2, accurately distinguishing pathogenic from benign variants.

RCoxNet improves cancer survival prediction by integrating mutation and clinical data with a deep learning Cox model and random walk algorithm, achieving better performance than existing models.

This study uses deep learning to analyze exome sequencing data and detect cancer-specific SNVs, addressing the challenge of mutation exclusivity in cancer.