Author

Anchit Kumar

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· 4

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Organism:plant (other)· 4 Research Focus:benchmarking· 3 Research Focus:gene regulatory network inference· 3 Organism:Arabidopsis· 2 Assay:bulk RNA-seq· 2 Research Focus:database / resource· 2 Assay:DNA methylation· 2

Frequent Co-Authors

Ravi ShankariD· 4 shared Sagar GuptaiD· 3 shared Umesh Bhati· 3 shared Veerbhan KesarwaniiD· 2 shared Akanksha SharmaiD· 1 shared Akanksha SharmaiD· 1 shared Gaurav Zinta· 1 shared Kirti Pandey· 1 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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4 shared papersBioinformatics

Ravi ShankarORCID

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4 papers

Recent·Most cited

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

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

Explainable Deep-Learning on condition specific expression profiles reveals critical cytosines in gene regulation

Veerbhan Kesarwani, Anchit Kumar, Akanksha Sharma, Sagar Gupta, Kirti Pandey, Gaurav Zinta, Ravi Shankar

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

bioRxiv · Jun 1, 2025 · PreprintBioinformatics