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Michael Heinzinger

Showing results (11-20 of 37) with videos related to

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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.
Computational and Structural Biotechnology Journal|December 22, 2022
From sequence to function through structure: Deep learning for protein designNoelia Ferruz, Michael Heinzinger, Mehmet Akdel, et al.
NAR Genomics and Bioinformatics|June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zoneMichael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Scientific Reports|October 8, 2022
Improving protein succinylation sites prediction using embeddings from protein language modelSuresh Pokharel, Pawel Pratyush, Michael Heinzinger, 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.
Pageof 4

Showing results (11-20 of 37) with videos related to

Sort By:
Pageof 4
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.
Computational and Structural Biotechnology Journal|December 22, 2022
From sequence to function through structure: Deep learning for protein designNoelia Ferruz, Michael Heinzinger, Mehmet Akdel, et al.
NAR Genomics and Bioinformatics|June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zoneMichael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Scientific Reports|October 8, 2022
Improving protein succinylation sites prediction using embeddings from protein language modelSuresh Pokharel, Pawel Pratyush, Michael Heinzinger, 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.
Pageof 4