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About IHBT

Research

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GitHub

Workshop/Conference

Career

Thursday October 09, 2025 9:58:24 am

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Research

Software & Web server

Lab Members

Publications

Workshop/Conference

GitHub

Careers

Qualification:

M.Sc. in Bioinformatics, Center of Bioinformatics, University of Allahabad, Prayagraj, UP

Area of Interest:

Deep learning, and Computational Regulomics.

Publications:

† Equally contributed * Corresponding Author

Conference Poster: 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*
22nd Horizons in Molecular Biology Symposium,
Max Planck Institute for Multidisciplinary Sciences, Gottingen, Germany, 2025
Find the conference poster here

Conference Poster: Decoding Plant TF-DNA Interactions Using PTFSpot and PTF-Vac: A Universal and Data-Independent Deep Learning Approach
Sagar Gupta, Jyoti, Umesh Bhati, Veerbhan Kesarwani, Akanksha Sharma, Ravi Shankar*
22nd Horizons in Molecular Biology Symposium,
Max Planck Institute for Multidisciplinary Sciences, Gottingen, Germany, 2025
Find the conference poster here

Conference Poster: The Genomics Armoury @ The Himalayan Centre for High-throughput Computational Biology
Sagar Gupta, Umesh Bhati, Jyoti, Veerbhan Kesarwani, Akanksha Sharma, Anchit Kumar, Ravi Shankar*
Genomics India Conference, IISc, Bengaluru, 2025 Find the conference poster here

Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation
Veerbhan Kesarwani, Anchit Kumar, Akanksha Sharma, Sagar Gupta, Ravi Shankar*
bioRxiv, 2025 Read the research article here

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*
Current Plant Biology, 2025 Read the research article here

DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants
Sagar Gupta, Anchit Kumar, Veerbhan Kesarwani, Ravi Shankar*
bioRxiv, 2025 Read the research article here

PTF-Vāc: 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*
Plant Communications, 2025 Read the research article here

Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery
Jyoti, Ritu, Sagar Gupta, Ravi Shankar*
Heliyon, 2024 Read the research article here

Genome wide identification of WUSHEL-related homeobox genes reveal their differential regulation during cold stress and in vitro organogenesis in Picrorhiza kurrooa
Jhilmil Nath, Shubham Joshi, Sagar Gupta, Veerbhan Kesarwani, Ravi Shankar and Rohit Joshi*
In Vitro Cellular & Developmental Biology - Plant, 2024 Read the research article here

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*
Briefings in Bioinformatics, 2024 Read the research article here

miWords: Transformers based composite deep-learning for highly accurate discovery of pre-miRNA regions across plant genomes
Sagar Gupta, Ravi Shankar*
Briefings in Bioinformatics, 2023 Read the research article here

DeepPlnc: Bi-modal Deep Learning for Highly Accurate Plant lncRNA Discovery
Ritu^†, Sagar Gupta^†, Nitesh Kumar Sharma, Ravi Shankar*
Genomics, 2022 Read the research article here

RBPSpot: Learning on Appropriate Contextual Information for RBP Binding Sites Discovery
Nitesh Kumar Sharma^†, Sagar Gupta, Prakash Kumar, Ashwani Kumar, Upendra Kumar Pradhan, Ravi Shankar*
iScience, 2021 Read the research article here

miRbiom: Machine-Learning on Bayesian Causal Nets of RBP-miRNA interactions successfully predicts miRNA profiles
Upendra Kumar Pradhan^†, Nitesh Kumar Sharma^†, Prakash Kumar, Ashwani Kumar, Sagar Gupta, Ravi Shankar*
PLoS ONE, 2021 Read the research article here

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