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Scientific Reports|January 14, 2021
Embeddings from deep learning transfer GO annotations beyond homologyMaria Littmann, Michael Heinzinger, Christian Dallago, et al.Scientific Reports|December 14, 2021
Protein embeddings and deep learning predict binding residues for various ligand classesMaria Littmann, Michael Heinzinger, Christian Dallago, et al.NAR Genomics and Bioinformatics|February 12, 2021
Family-specific analysis of variant pathogenicity prediction toolsJan Zaucha, Michael Heinzinger, Svetlana Tarnovskaya, et al.NAR Genomics and Bioinformatics|June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zoneMichael Heinzinger, Maria Littmann, Ian Sillitoe, et al.Journal of Molecular Biology|March 7, 2020
ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequenceJiajun Qiu, Michael Bernhofer, Michael Heinzinger, et al.Bioinformatics (Oxford, England)|May 12, 2021
Clustering FunFams using sequence embeddings improves EC purityMaria Littmann, Nicola Bordin, Michael Heinzinger, et al.Journal of Molecular Biology|March 26, 2025
ProtSpace: A Tool for Visualizing Protein SpaceTobias Senoner, Tobias Olenyi, Michael Heinzinger, et al.Journal of Molecular Biology|March 26, 2025
TMVisDB: Annotation and 3D-visualization of Transmembrane ProteinsTobias Olenyi, Céline Marquet, Anastasia Grekova, et al.BMC Bioinformatics|December 19, 2019
Modeling aspects of the language of life through transfer-learning protein sequencesMichael Heinzinger, Ahmed Elnaggar, Yu Wang, et al.NAR Genomics and Bioinformatics|December 5, 2024
Bilingual language model for protein sequence and structureMichael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, et al.Pageof 25