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Michael Heinzinger
Michael Heinzinger
Verified email at tum.de
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Cited by
Year
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
1599*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, 1-17, 2019
5552019
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
2132021
Embeddings from deep learning transfer GO annotations beyond homology
M Littmann, M Heinzinger, C Dallago, T Olenyi, B Rost
Scientific reports 11 (1), 1160, 2021
1302021
Embeddings from protein language models predict conservation and variant effects
C Marquet, M Heinzinger, T Olenyi, C Dallago, K Erckert, M Bernhofer, ...
Human genetics 141 (10), 1629-1647, 2022
1152022
Light attention predicts protein location from the language of life
H Stärk, C Dallago, M Heinzinger, B Rost
Bioinformatics Advances 1 (1), vbab035, 2021
1062021
Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction
K Weissenow, M Heinzinger, B Rost
Structure 30 (8), 1169-1177. e4, 2022
1002022
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
952020
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), 23916, 2021
902021
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
882021
From sequence to function through structure: Deep learning for protein design
N Ferruz, M Heinzinger, M Akdel, A Goncearenco, L Naef, C Dallago
Computational and Structural Biotechnology Journal 21, 238-250, 2023
812023
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
792022
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
N Bordin, I Sillitoe, V Nallapareddy, C Rauer, SD Lam, VP Waman, N Sen, ...
Communications biology 6 (1), 160, 2023
682023
Bilingual language model for protein sequence and structure
M Heinzinger, K Weissenow, JG Sanchez, A Henkel, M Mirdita, ...
bioRxiv, 2023.07. 23.550085, 2023
592023
Novel machine learning approaches revolutionize protein knowledge
N Bordin, C Dallago, M Heinzinger, S Kim, M Littmann, C Rauer, ...
Trends in Biochemical Sciences 48 (4), 345-359, 2023
502023
Others Prottrans: toward understanding the language of life through self-supervised learning
A Elnaggar, M Heinzinger, C Dallago, G Rehawi, Y Wang, L Jones, ...
IEEE Trans Pattern Anal Mach Intell 44, 7112-27, 2021
402021
Improving protein succinylation sites prediction using embeddings from protein language model
S Pokharel, P Pratyush, M Heinzinger, RH Newman, DB Kc
Scientific reports 12 (1), 16933, 2022
382022
SETH predicts nuances of residue disorder from protein embeddings
D Ilzhoefer, M Heinzinger, B Rost
bioRxiv, 2022
332022
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models
V Nallapareddy, N Bordin, I Sillitoe, M Heinzinger, M Littmann, VP Waman, ...
Bioinformatics 39 (1), btad029, 2023
312023
Nearest neighbor search on embeddings rapidly identifies distant protein relations
K Schütze, M Heinzinger, M Steinegger, B Rost
Frontiers in Bioinformatics 2, 1033775, 2022
292022
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