Pallavi Surana

PGViS quantifies non-coding germline regulatory risk in NSCLC by integrating variant-level signals with population reference data.

The paper introduces STPCaT, an isoform-centric analysis of solid tumors, revealing cancer-specific and normal-specific isoform groups and identifying new cancer-testis antigens with diagnostic potential.

TSProm identifies tissue-specific gene regulatory elements by training specialized DNA models and using explainable AI to analyze promoter sequences in human tissues.

HViLM is a foundation model for viral genomics that predicts pathogenicity, transmissibility, and host tropism across multiple viral families with high accuracy.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.