Veerbhan Kesarwani

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

DMRU is a generative deep-learning model that identifies condition-specific DNA methylation in plants with high accuracy across species and conditions.

PTF-Vac discovers plant transcription factor binding sites using a generative deep learning model that outperforms existing methods and is independent of species and predefined motifs.

The paper uses deep learning to identify critical cytosines in gene regulation across plant species by analyzing methylation and expression data.

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.

The paper analyzes SARS-CoV-2 transcriptome data to identify immunodominant epitopes for vaccine design through epitope prediction and molecular docking studies.