Tirtharaj Dash

The paper develops an LLM-based method for generating feasible lead molecules for DBH inhibitors, achieving comparable affinity to clinical trial drugs.

This study integrates copy number and mutation data with gene expression to identify prognostic and diagnostic biomarkers for colorectal cancer.

The paper trains a deep learning model on yeast promoter sequences to predict gene expression and identifies cis-regulatory elements using explainable AI.

This paper introduces LMLF, a method using logical feedback to guide LLMs in generating drug leads for specific targets, showing improved binding affinities and potential novelty.

The paper refines a gene signature for identifying TAM polarization in CRC using single-cell RNA-seq and transfer learning, validated across multiple datasets.

The paper combines deep neural networks and Inductive Logic Programming to generate potential inhibitors for targets with limited data, using domain knowledge to improve molecule generation.