Author

Ramit Bharanikumar

Adaptive Biotechnologies (United States)

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· 3 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Ashok PalaniappaniD· 2 shared Keshav Aditya R. Premkumar· 2 shared Ali DavariashtiyaniiD· 1 shared Aly Azeem KhaniD· 1 shared Ben LaiiD· 1 shared Bryan Howie· 1 shared Eric Vincent Marietta· 1 shared Govind BhagatiD· 1 shared

Activity by Year

2018-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersBioinformatics

Ashok PalaniappanORCID

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Author profile →

4 papers

Recent·Most cited

DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions

Ben Lai, Melissa Englund, Ramit Bharanikumar, Isabel Nocedal, Ali Davariashtiyani, Jason F. Perera, Aly Azeem Khan

DecoderTCR improves TCR-pMHC interaction prediction using pretraining and entropy-guided decoding, achieving high AUROC scores without epitope-specific training.

bioRxiv · Feb 6, 2026 · PreprintBioinformatics

Shared TCRs in peripheral blood offer robust celiac disease classification independent of gluten Intake

Rebecca Elyanow, Rok Seon Choung, Eric Vincent Marietta, Ramit Bharanikumar, Wenyu Zhou, Haiyin Chen‐Harris, Govind Bhagat, Peter H. Green, Bryan Howie, Harlan S. Robins, Susan L. Neuhausen, Joseph A. Murray

The paper identifies CeD-specific TCRs in peripheral blood that classify celiac disease independently of gluten intake, showing higher predictive power than traditional TCRs.

bioRxiv · Jan 14, 2026 · PreprintImmunology

Riboflow: using deep learning to classify riboswitches with ~99% accuracy

Keshav Aditya R. Premkumar, Ramit Bharanikumar, Ashok Palaniappan

The paper introduces a deep learning model called Riboflow that classifies riboswitches with over 99% accuracy using CNN and LSTM architectures.

Frontiers in Bioengineering and Biotechnology · Jul 14, 2020Peer-reviewedBioinformatics

PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence

Ramit Bharanikumar, Keshav Aditya R. Premkumar, Ashok Palaniappan

PromoterPredict models Escherichia coli σ70 promoter strength using sequence data, finding a logarithmic relationship between sequence features and promoter strength.

PeerJ · Nov 7, 2018Peer-reviewedBioinformatics