Author

Jeppe Hallgren

ORCID 0000-0001-5377-2643Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Organism:multiple species· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Anders KroghiD· 2 shared Henrik NielseniD· 2 shared Konstantinos D. TsirigosiD· 2 shared Ole WintheriD· 2 shared Felix TeufeliD· 1 shared José Juan Almagro ArmenterosiD· 1 shared Mads D. Pedersen· 1 shared Paolo MarcatiliiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Anders KroghORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 27, 2026

DeepTMHMM2 enables accurate prediction of transmembrane protein topology and subcellular location

Felix Teufel, Jeppe Hallgren, Henrik Nielsen…·Ole Winther

DeepTMHMM2 predicts transmembrane protein topology and subcellular location, including re-entrant regions and interfacial helices, and identifies localization across 17 membranes.

DOI 10.64898/2026.08.24.746435

proteomicsdeep learning

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BioinformaticsbioRxivPreprintApr 8, 2022

✦ In-depth digest

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Jeppe Hallgren, Konstantinos D. Tsirigos, Mads D. Pedersen…·Ole Winther

Combining an ESM-1b encoder with a conditional random field decoder, DeepTMHMM classifies alpha-helical and beta-barrel transmembrane proteins and their topology, outperforming most compared tools, including on signal-peptide cleavage sites.

1,528 citations

multiple speciesprotein sequence

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