Author

Smruti Panda

Indraprastha Institute of Information Technology Delhi

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· 1 Cancer Biology· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:human· 3 Research Focus:variant effect prediction· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Debarka SenguptaiD· 3 shared Gaurav AhujaiD· 3 shared Abhishek HalderiD· 2 shared Bernadette MathewiD· 2 shared Prashant GuptaiD· 2 shared Sakshi Gujral· 2 shared Sreeram Chandra Murthy PeelaiD· 2 shared Stuti KumariiD· 2 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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3 shared papersEvolutionary Biology

Debarka SenguptaORCID

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Author profile →

3 papers

Recent·Most cited

Intra-DNA k-mer Conservation Patterns Encode Evolutionary Selection of Variants

Bernadette Mathew, Abhishek Halder, Nancy Jaiswal, Smruti Panda, Debjit Pramanik, Sreeram Chandra Murthy Peela, Abhishek Dinkarnath Garg, Sadhana Tripathi, Swarnava Samanta, Prashant Gupta, Vandana Malhotra, Gaurav Ahuja, Debarka Sengupta

The paper introduces kGain, a metric quantifying k-mer frequency shifts due to mutations, and shows it predicts adaptive potential across species and experimental systems.

iScience · Nov 1, 2026Peer-reviewedEvolutionary Biology

Mechanism-aware inference of response to targeted cancer therapies

Nilabja Bhattacharjee, Sreeram Chandra Murthy Peela, Abhishek Halder, Bernadette Mathew, Swarnava Samanta, Sakshi Gujral, Stuti Kumari, Smruti Panda, Ritwik Ganguly, Gaurav Ahuja, Subhajyoti De, Angshul Majumder, Debarka Sengupta

This paper introduces FORGE, a framework that jointly models drug response and gene essentiality to identify treatment groups and predict efficacy using basal gene expression.

bioRxiv · Sep 4, 2025 · PreprintCancer Biology

RCoxNet: deep learning framework for enhanced cancer survival prediction integrating random walk with restart with mutation and clinical data

Stuti Kumari, Sakshi Gujral, Smruti Panda, Prashant Gupta, Gaurav Ahuja, Debarka Sengupta

RCoxNet improves cancer survival prediction by integrating mutation and clinical data with a deep learning Cox model and random walk algorithm, achieving better performance than existing models.

bioRxiv · Sep 17, 2024 · PreprintBioinformatics