Author

Rohit Bhattacharya

Johns Hopkins UniversityORCID 0000-0002-0555-9714Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Collin J. TokheimiD· 2 shared Rachel KarchiniD· 2 shared Ashok Sivakumar· 1 shared Christopher Blair DouvilleiD· 1 shared David L. Masica· 1 shared Kyle Moad· 1 shared Michaël RyaniD· 1 shared RyangGuk Kim· 1 shared

Activity by Year

2017-2017 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Collin J. TokheimORCID

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2 papers

Recent·Most cited

BioinformaticsCancer ResearchPeer-reviewedOct 31, 2017

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

David L. Masica, Christopher Blair Douville, Collin J. Tokheim…·Rachel Karchin

CRAVAT 4 provides tools for analyzing and interpreting somatic mutations in cancer studies, offering annotation, prioritization, and visualization with a web interface.

74 citations

whole-exome sequencingstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintJul 27, 2017

Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins

Rohit Bhattacharya, Ashok Sivakumar, Collin J. Tokheim…·Rachel Karchin

This study evaluates machine learning methods for predicting peptide-MHC Class I binding affinities, finding that MHCnuggets performs similarly to existing methods but is faster and handles variable-length peptides.

38 citations

machine learning (classical)benchmarking

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