Author

Aarsh Shekhar

The University of Texas at Austin

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

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Organism:human· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Assay:proteomics· 1

Frequent Co-Authors

Anish Sundar· 2 shared Rahul MahapatraiD· 2 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

2 top collaborators · 1 mutual co-author link

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2 shared papersBioinformatics

Anish Sundar

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2 papers

Recent·Most cited

A Computational Pipeline for Glioblastoma Vaccine Development: Integrating Novel Omics-Driven OIP5 Target Discovery to Create a Deep Learning-Based Immunogenicity Framework for Personalized Immunotherapy

Rahul Mahapatra, Anish Sundar, Aarsh Shekhar

This paper introduces TEIP, a deep learning framework for predicting immunogenic epitopes in glioblastoma by integrating omics data and molecular features, outperforming existing methods.

bioRxiv · Dec 25, 2025 · PreprintBioinformatics

TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Cancer Using Deep Learning and Multi-Modal Biological Features

Rahul Mahapatra, Anish Sundar, Aarsh Shekhar

TEIP predicts tumor epitope immunogenicity in glioblastoma using deep learning and multi-modal biological features, outperforming existing methods and identifying clinically relevant antigens.

bioRxiv · Nov 9, 2025 · PreprintCancer Biology