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Scientific Reports
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January 14, 2021
Embeddings from deep learning transfer GO annotations beyond homology
Maria Littmann, Michael Heinzinger, Christian Dallago, et al.
Scientific Reports
|
December 14, 2021
Protein embeddings and deep learning predict binding residues for various ligand classes
Maria Littmann, Michael Heinzinger, Christian Dallago, et al.
NAR Genomics and Bioinformatics
|
February 12, 2021
Family-specific analysis of variant pathogenicity prediction tools
Jan 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 design
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, et al.
NAR Genomics and Bioinformatics
|
June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zone
Michael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Scientific Reports
|
October 8, 2022
Improving protein succinylation sites prediction using embeddings from protein language model
Suresh 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 sequence
Jiajun Qiu, Michael Bernhofer, Michael Heinzinger, et al.
Bioinformatics (Oxford, England)
|
May 12, 2021
Clustering FunFams using sequence embeddings improves EC purity
Maria Littmann, Nicola Bordin, Michael Heinzinger, et al.
Journal of Molecular Biology
|
March 26, 2025
ProtSpace: A Tool for Visualizing Protein Space
Tobias Senoner, Tobias Olenyi, Michael Heinzinger, et al.
Journal of Molecular Biology
|
March 26, 2025
TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins
Tobias Olenyi, Céline Marquet, Anastasia Grekova, et al.
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of 4
Search research articles
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Showing results (11-20 of 37) with videos related to
Sort By:
Page
of 4
Scientific Reports
|
January 14, 2021
Embeddings from deep learning transfer GO annotations beyond homology
Maria Littmann, Michael Heinzinger, Christian Dallago, et al.
Scientific Reports
|
December 14, 2021
Protein embeddings and deep learning predict binding residues for various ligand classes
Maria Littmann, Michael Heinzinger, Christian Dallago, et al.
NAR Genomics and Bioinformatics
|
February 12, 2021
Family-specific analysis of variant pathogenicity prediction tools
Jan 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 design
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, et al.
NAR Genomics and Bioinformatics
|
June 15, 2022
Contrastive learning on protein embeddings enlightens midnight zone
Michael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
Scientific Reports
|
October 8, 2022
Improving protein succinylation sites prediction using embeddings from protein language model
Suresh 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 sequence
Jiajun Qiu, Michael Bernhofer, Michael Heinzinger, et al.
Bioinformatics (Oxford, England)
|
May 12, 2021
Clustering FunFams using sequence embeddings improves EC purity
Maria Littmann, Nicola Bordin, Michael Heinzinger, et al.
Journal of Molecular Biology
|
March 26, 2025
ProtSpace: A Tool for Visualizing Protein Space
Tobias Senoner, Tobias Olenyi, Michael Heinzinger, et al.
Journal of Molecular Biology
|
March 26, 2025
TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins
Tobias Olenyi, Céline Marquet, Anastasia Grekova, et al.
Page
of 4