Author

Umesh Bhati

Institute of Himalayan Bioresource Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 6

Signature Assays, Organisms & Methods

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Approach:deep learning· 6 Organism:plant (other)· 5 Research Focus:benchmarking· 4 Research Focus:gene regulatory network inference· 4 Assay:DNA methylation· 3 Assay:ChIP-seq· 2 Research Focus:database / resource· 2 Research Focus:differential expression· 2

Frequent Co-Authors

Ravi ShankariD· 6 shared Veerbhan KesarwaniiD· 4 shared Anchit Kumar· 3 shared Sagar GuptaiD· 3 shared Sagar GuptaiD· 3 shared Akanksha SharmaiD· 2 shared Jyoti· 2 shared Upendra Kumar PradhaniD· 1 shared

Activity by Year

2024-2026 · peak 3/yr

Co-Author Collaboration Network

8 top collaborators · 23 mutual co-author links

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6 shared papersBioinformatics

Ravi ShankarORCID

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6 papers

Recent·Most cited

ProMaya: a hierarchical universal Deep Learning framework for accurate and interpretable Protein-Protein interaction identification

Umesh Bhati, Sagar Gupta, Veerbhan Kesarwani, Ravi Shankar

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

bioRxiv · Sep 8, 2026 · PreprintBioinformatics

GANGE: Achieving Sequencing Without Sequencing With Diffusion Guided Generative Genomic Transformer

Sagar Gupta, Anchit Kumar, Umesh Bhati, Ravi Shankar

GANGE is a deep-learning system that generates accurate genomic sequences from low-coverage, error-prone reads, reducing sequencing costs and enabling regulomics research without genome data.

bioRxiv · Apr 17, 2026 · PreprintBioinformatics

DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants

Sagar Gupta, Anchit Kumar, Veerbhan Kesarwani, Umesh Bhati, Ravi Shankar

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

Briefings in Bioinformatics · Nov 1, 2025Peer-reviewedBioinformatics

PTF-Vac: An explainable and generative deep co-learning encoders-decoders system for ab-initio discovery of plant transcription factor binding sites

Sagar Gupta, Jyoti, Umesh Bhati, Veerbhan Kesarwani, Akanksha Sharma, Ravi Shankar

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.

Plant Communications · Oct 1, 2025Peer-reviewedBioinformatics

Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning

Umesh Bhati, Akanksha Sharma, Sagar Gupta, Anchit Kumar, Upendra Kumar Pradhan, Ravi Shankar

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.

Current Plant Biology · Aug 5, 2025Peer-reviewedBioinformatics

PTFSpot: Deep co-learning on transcription factors and their binding regions attains impeccable universality in plants

Sagar Gupta, Veerbhan Kesarwani, Umesh Bhati, Jyoti, Ravi Shankar

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.

Briefings in Bioinformatics · May 23, 2024Peer-reviewedBioinformatics