Author

Martin Closter Jespersen

Technical University of Denmark

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

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Kamilla Kjærgaard Jensen· 2 shared Morten NielseniD· 2 shared Paolo MarcatiliiD· 2 shared Vanessa Isabell JurtziD· 2 shared Amalie Kai BentzeniD· 1 shared Bent PeterseniD· 1 shared Bjoern PetersiD· 1 shared Casper Kaae Sønderby· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Kamilla Kjærgaard Jensen

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2 papers

Recent·Most cited

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

Michael Schantz Klausen, Martin Closter Jespersen, Henrik Nielsen, Kamilla Kjærgaard Jensen, Vanessa Isabell Jurtz, Casper Kaae Sønderby, Morten Otto Alexander Sommer, Ole Winther, Morten Nielsen, Bent Petersen, Paolo Marcatili

NetSurfP-2.0 improves prediction of protein structural features with higher accuracy and faster runtime than previous versions.

Proteins Structure Function and Bioinformatics · Feb 20, 2019Peer-reviewedBioinformatics

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