Author

T Dhanushkumar

REVA University

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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Cancer Biology· 3 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1

Frequent Co-Authors

Karthick VasudevaniD· 4 shared B G Sunila· 2 shared Prasanna Kumar Selvam· 2 shared Rohini KarunakaraniD· 2 shared Sripad Rama Hebbar· 2 shared Anbarasu KrishnaniD· 1 shared Krishnan AnbarasuiD· 1 shared Pasupuleti Visweswara Rao· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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4 shared papersCancer Biology

Karthick VasudevanORCID

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Author profile →

4 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

Network-based integration of gene expression and DNA methylation identifies prognostic biomarkers for early-stage pancreatic cancer

T Dhanushkumar, Krishnan Anbarasu, Sripad Rama Hebbar, Karthick Vasudevan, Rohini Karunakaran

The paper integrates gene expression and DNA methylation data using a network-based approach to identify prognostic biomarkers for early-stage pancreatic cancer, achieving higher classification accuracy with multi-omics features.

bioRxiv · Feb 11, 2026 · PreprintCancer Biology

DeepEpitope: Leveraging Transformation-Based protein Embeddings for Accurate linear Cancer B-cell Epitope Identification

T Dhanushkumar, Pasupuleti Visweswara Rao, Karthick Vasudevan

DeepEpitope predicts linear B-cell epitopes from cancer antigens using deep learning and ESM embeddings, achieving higher accuracy than existing tools.

bioRxiv · May 23, 2025 · PreprintCancer Biology

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