Author

Anish Sundar

Austin Community College

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· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Organism:human· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Aarsh Shekhar· 2 shared Rahul MahapatraiD· 2 shared Arnav RajeeviD· 1 shared Soumith Veeragoni· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

4 top collaborators · 2 mutual co-author links

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2 shared papersBioinformatics

Aarsh Shekhar

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3 papers

Recent·Most cited

Integrated Transcriptomic and CRISPR Dependency Analysis Prioritizes a CDK1-AURKB Mitotic Vulnerability Axis in Diffuse Intrinsic Pontine Glioma

Arnav Rajeev, Soumith Veeragoni, Anish Sundar

The paper integrates transcriptomic and CRISPR data to identify mitotic vulnerabilities in DIPG, highlighting CDK1 and AURKB as potential therapeutic targets.

bioRxiv · Sep 1, 2026 · PreprintBioinformatics

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