Author

Rajan R. Chaudhari

The University of Texas MD Anderson Cancer CenterORCID 0000-0002-6038-0098Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:drug response / drug discovery· 3 Research Focus:differential expression· 1 Organism:mouse· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Beibei HuangiD· 2 shared Anna LechneriD· 1 shared Bethany K. OkadaiD· 1 shared Christopher J. RosarioiD· 1 shared Eric ZhangiD· 1 shared Gabriella Costa Machado da CruziD· 1 shared Heiko GimperleiniD· 1 shared Jacob Stuart Lamson· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Beibei HuangORCID

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Author profile →

3 papers

Recent·Most cited

Overcoming Immune Checkpoint Inhibitor Resistance via Potent and Selective Dual αvβ6/8 Inhibitors Based on Engineered Lasso Peptides

Anna Lechner, Peter A. Jordan, Gabriella Costa Machado da Cruz, Jacob Stuart Lamson, Jessica Gordon, Bethany K. Okada, Kelsey Anderson, Rajan R. Chaudhari, Christopher J. Rosario, Jasmine Mikesell, Scott A. McPhee, Mark J. Burk

Journal of the American Chemical Society · Sep 2, 2025Peer-reviewedCancer Biology

Sequence-based Optimized Chaos Game Representation and Deep Learning for Peptide/Protein Classification

Beibei Huang, Eric Zhang, Rajan R. Chaudhari, Heiko Gimperlein

The paper develops a sequence-based chaos game representation combined with deep learning for peptide/protein classification, showing superior performance in hemolytic/non-hemolytic peptide and HIV protease mutant classification.

bioRxiv · Sep 10, 2022 · PreprintBioinformatics

JavaDL: a Java-based Deep Learning Tool to Predict Drug Responses

Beibei Huang, Lon Wolf R. Fong, Rajan R. Chaudhari, Zhi Tan, Shuxing Zhang

JavaDL predicts cancer drug responses using chemical features with a Java-based deep learning model, achieving high accuracy and robustness in big data analysis.

bioRxiv · May 4, 2020 · PreprintBioinformatics