| 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 | 1106* | 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 | 448 | 2019 |
| 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 | 168 | 2021 |
| 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 | 108 | 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 | 84 | 2021 |
| 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 | 76 | 2022 |
| 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 | 76 | 2022 |
| 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 | 71 | 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 | 71 | 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 | 68 | 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 | 55 | 2022 |
| 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 | 52 | 2023 |
| 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 | 51 | 2023 |
| 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 | 30 | 2023 |
| SETH predicts nuances of residue disorder from protein embeddings D Ilzhoefer, M Heinzinger, B Rost bioRxiv, 2022 | 24 | 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 | 24 | 2021 |
| 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 | 23 | 2023 |
| ProstT5: Bilingual language model for protein sequence and structure M Heinzinger, K Weissenow, JG Sanchez, A Henkel, M Steinegger, ... bioRxiv, 2023.07. 23.550085, 2023 | 22 | 2023 |
| Nearest neighbor search on embeddings rapidly identifies distant protein relations K Schütze, M Heinzinger, M Steinegger, B Rost Frontiers in Bioinformatics 2, 1033775, 2022 | 22 | 2022 |
| 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 | 20 | 2022 |
Burkhard RostProfessor of Computer Science & Computational Biology & Bioinformatics, TUM, MunichVerified email at tum.de
