Author

Jyoti

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· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:DNA methylation· 3 Organism:plant (other)· 3 Assay:ChIP-seq· 2 Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Approach:benchmark study· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Ravi ShankariD· 3 shared Sagar GuptaiD· 2 shared Umesh Bhati· 2 shared Veerbhan KesarwaniiD· 2 shared Akanksha SharmaiD· 1 shared Ritu RituiD· 1 shared Sagar GuptaiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 15 mutual co-author links

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3 shared papersBioinformatics

Ravi ShankarORCID

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3 papers

Recent·Most cited

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

Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery

Jyoti, Ritu Ritu, Sagar Gupta, Ravi Shankar

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

Heliyon · Oct 1, 2024Peer-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