Michael Heinzinger
Michael Heinzinger
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ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing
A Elnaggar, M Heinzinger, C Dallago, G Rihawi, Y Wang, L Jones, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Modeling aspects of the language of life through transfer-learning protein sequences
M Heinzinger, A Elnaggar, Y Wang, C Dallago, D Nechaev, F Matthes, ...
BMC bioinformatics 20 (1), 1-17, 2019
Embeddings from deep learning transfer GO annotations beyond homology
M Littmann, M Heinzinger, C Dallago, T Olenyi, B Rost
Scientific reports 11 (1), 1-14, 2021
PredictProtein-predicting protein structure and function for 29 years
M Bernhofer, C Dallago, T Karl, V Satagopam, M Heinzinger, M Littmann, ...
Nucleic acids research 49 (W1), W535-W540, 2021
ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence
J Qiu, M Bernhofer, M Heinzinger, S Kemper, T Norambuena, F Melo, ...
Journal of molecular biology 432 (7), 2428-2443, 2020
Learned embeddings from deep learning to visualize and predict protein sets
C Dallago, K Schütze, M Heinzinger, T Olenyi, M Littmann, AX Lu, ...
Current Protocols 1 (5), e113, 2021
Light attention predicts protein location from the language of life
H Stärk, C Dallago, M Heinzinger, B Rost
Bioinformatics Advances 1 (1), vbab035, 2021
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
K Weißenow, M Heinzinger, B Rost
Structure, 2022
Clustering FunFams using sequence embeddings improves EC purity
M Littmann, N Bordin, M Heinzinger, K Schütze, C Dallago, C Orengo, ...
Bioinformatics 37 (20), 3449-3455, 2021
End-to-end multitask learning, from protein language to protein features without alignments
A Elnaggar, M Heinzinger, C Dallago, B Rost
bioRxiv, 864405, 2020
Embeddings from protein language models predict conservation and variant effects
C Marquet, M Heinzinger, T Olenyi, C Dallago, K Erckert, M Bernhofer, ...
Human genetics, 1-19, 2021
Protein embeddings and deep learning predict binding residues for various ligand classes
M Littmann, M Heinzinger, C Dallago, K Weissenow, B Rost
Scientific Reports 11 (1), 1-15, 2021
Family-specific analysis of variant pathogenicity prediction tools
J Zaucha, M Heinzinger, S Tarnovskaya, B Rost, D Frishman
NAR genomics and bioinformatics 2 (2), lqaa014, 2020
Dark proteins important for cellular function
A Schafferhans, SI O'Donoghue, M Heinzinger, B Rost
Proteomics 18 (21-22), 1800227, 2018
CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models
V Nallapareddy, N Bordin, I Sillitoe, M Heinzinger, M Littmann, V Waman, ...
bioRxiv, 2022
Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins
J Zaucha, M Heinzinger, A Kulandaisamy, E Kataka, ÓL Salvádor, ...
Briefings in Bioinformatics 22 (3), bbaa132, 2021
Contrastive learning on protein embeddings enlightens midnight zone
M Heinzinger, M Littmann, I Sillitoe, N Bordin, C Orengo, B Rost
NAR genomics and bioinformatics 4 (2), lqac043, 2022
Bee core venom genes predominantly originated before aculeate stingers evolved
I Koludarov, M Velasque, T Timm, G Lochnit, M Heinzinger, A Vilcinskas, ...
bioRxiv, 2022
The identification of microplastics based on vibrational spectroscopy data–A critical review of data analysis routines
J Weisser, T Pohl, M Heinzinger, NP Ivleva, T Hofmann, K Glas
TrAC Trends in Analytical Chemistry, 116535, 2022
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
N Bordin, I Sillitoe, MV Nallapareddy, C Rauer, SD Lam, VP Waman, ...
bioRxiv, 2022
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