Author

Prasanna Kumar Selvam

Manipal Academy of Higher Education

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 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Approach:benchmark study· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Karthick VasudevaniD· 3 shared B G Sunila· 2 shared Supraja Mohan· 2 shared T Dhanushkumar· 2 shared Anbarasu KrishnaniD· 1 shared Rohini KarunakaraniD· 1 shared Santhosh Mudipalli ElavarasuiD· 1 shared Sripad Rama Hebbar· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 24 mutual co-author links

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3 shared papersCancer Biology

Karthick VasudevanORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Transcriptomics and mutational analysis to screen immunogenic neoantigen peptides and Patient stratification based on immune subtypes for TNBC

Karthick Vasudevan, T Dhanushkumar, Prasanna Kumar Selvam, Anbarasu Krishnan, B G Sunila, Santhosh Mudipalli Elavarasu, Supraja Mohan, Rohini Karunakaran

This paper identifies neoantigen peptides and immune subtypes in TNBC using genomic and transcriptomic data, finding potential vaccine targets and biomarkers for immune stratification.

bioRxiv · Feb 19, 2026 · PreprintCancer Biology

Emergence of Dual-Risk Klebsiella pneumoniae: Global Hotspots of Convergence Between Resistance and Hypervirulence

Prasanna Kumar Selvam, Supraja Mohan, Karthick Vasudevan

The paper analyzes K. pneumoniae genomes to show convergence between antimicrobial resistance and hypervirulence, identifying dual-risk clones and geographic hotspots.

bioRxiv · Nov 13, 2025 · PreprintGenomics

VaxOptiML: Leveraging Machine Learning for Accurate Prediction of MHC-I & II Epitopes for Optimized Cancer Immunotherapy

T Dhanushkumar, B G Sunila, Sripad Rama Hebbar, Prasanna Kumar Selvam, Karthick Vasudevan

VaxOptiML predicts MHC-I and MHC-II epitopes using machine learning, offering higher accuracy and usability for cancer immunotherapy.

Immunogenetics · Dec 7, 2024Peer-reviewedBioinformatics