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

BioinformaticsProteins Structure Function and BioinformaticsPeer-reviewedFeb 20, 2019

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

Michael Schantz Klausen, Martin Closter Jespersen, Henrik Nielsen…·Paolo Marcatili

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

574 citationstop 1%

protein sequencedeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintOct 3, 2018

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

Vanessa Isabell Jurtz, Leon Eyrich Jessen, Amalie Kai Bentzen…·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.

118 citations

humanprotein sequence

𝕏 PostBluesky