Author

Swapnil Mahajan

Centre National de la Recherche ScientifiqueORCID 0000-0003-2136-1801Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:deep learning· 1 Organism:human· 1 Assay:protein sequence· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Alexandre G. de BreverniD· 1 shared Amalie Kai BentzeniD· 1 shared Bernard OffmanniD· 1 shared Bjoern PetersiD· 1 shared Frédéric CadetiD· 1 shared Iyanar VetriveliD· 1 shared Kamilla Kjærgaard Jensen· 1 shared Leon Eyrich JesseniD· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Alexandre G. de BrevernORCID

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2 papers

Recent·Most cited

NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks

Vanessa Isabell Jurtz, Leon Eyrich Jessen, Amalie Kai Bentzen, Martin Closter Jespersen, Swapnil Mahajan, Randi Vita, Kamilla Kjærgaard Jensen, Paolo Marcatili, Sine Reker Hadrup, Bjoern Peters, Morten Nielsen

A sequence-only convolutional network trained on T cell receptor (TCR)-peptide pairs for HLA-A*02:01 can single out TCRs that bind a target peptide among many non-binding ones.

bioRxiv · Oct 3, 2018 · PreprintBioinformatics

PB-kPRED: Knowledge-Based Prediction Of Protein Backbone Conformation Using A Structural Alphabet

Iyanar Vetrivel, Swapnil Mahajan, Manoj Tyagi, Lionel Hoffmann, Yves‐Henri Sanejouand, Narayanaswamy Srinivasan, Alexandre G. de Brevern, Frédéric Cadet, Bernard Offmann

The paper introduces kPred, a knowledge-based method for predicting protein backbone conformations using structural prototypes, achieving mean Q16 accuracies between 40.8% and 66.3%.

PLoS ONE · Nov 21, 2017Peer-reviewedBioinformatics