Sagar Gupta

ProMaya identifies protein-protein interactions using a hierarchical graph-transformer framework, achieving >95% accuracy across nine species and outperforming existing tools by >12%.

The paper introduces CTF-BIND, a deep learning framework that detects TF binding from RNA-seq data without ChIP-seq, achieving 93% accuracy in plant stress response analysis.

This paper evaluates computational methods for identifying transcription factor binding regions in plants, identifies issues with existing tools, fixes them, and provides corrected scripts.

PTFSpot uses a transformer-based deep learning approach to universally detect transcription factor binding regions in plants, outperforming existing tools by >30% and achieving >90% accuracy across unencountered species and TF families.

miRbiom uses Bayesian causal networks of RBP-miRNA interactions to predict miRNA profiles with high accuracy without requiring separate miRNA profiling experiments.