| 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 | 167 | 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 | 107 | 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), 23916, 2021 | 71 | 2021 |
| 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 |
| Validity of machine learning in biology and medicine increased through collaborations across fields of expertise M Littmann, K Selig, L Cohen-Lavi, Y Frank, P Hönigschmid, E Kataka, ... Nature Machine Intelligence 2 (1), 18-24, 2020 | 57 | 2020 |
| 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 |
| 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 | 28 | 2023 |
| Common sequence variants affect molecular function more than rare variants? Y Mahlich, J Reeb, M Hecht, M Schelling, TAP De Beer, Y Bromberg, ... Scientific reports 7 (1), 1608, 2017 | 25 | 2017 |
| 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 |
| FunFam protein families improve residue level molecular function prediction L Scheibenreif, M Littmann, C Orengo, B Rost BMC bioinformatics 20, 1-9, 2019 | 20 | 2019 |
| Evolutionary couplings and sequence variation effect predict protein binding sites M Schelling, TA Hopf, B Rost Proteins: Structure, Function, and Bioinformatics 86 (10), 1064-1074, 2018 | 18 | 2018 |
| LambdaPP: Fast and accessible protein‐specific phenotype predictions T Olenyi, C Marquet, M Heinzinger, B Kröger, T Nikolova, M Bernhofer, ... Protein Science 32 (1), e4524, 2023 | 7 | 2023 |
| Detailed prediction of protein sub-nuclear localization M Littmann, T Goldberg, S Seitz, M Bodén, B Rost BMC bioinformatics 20, 1-15, 2019 | 7 | 2019 |
| Alphafold2 reveals commonalities and novelties in protein structure space for 21 model organisms. bioRxiv, 2022 N Bordin, I Sillitoe, V Nallapareddy, C Rauer, SD Lam, VP Waman, N Sen, ... URL https://www. biorxiv. org/content/early/2022/06/03/2022.06 2, 0 | 2 | |
| Refining Embedding-Based Binding Predictions by Leveraging AlphaFold2 Structures L Endres, T Olenyi, K Erckert, K Weißenow, B Rost, M Littmann bioRxiv, 2022.08. 31.505997, 2022 | 1 | 2022 |
| Deep learning cluster analysis reveals subtypes in response to antisense oligonucleotide therapy in chronic hepatitis B C Weis, M Littmann, A Triastcyn, W Jordan, D Theodore, M Paff, H Tipney, ... Journal of Hepatology 78, S1151-S1152, 2023 | | 2023 |
| Prediction of Protein Function through Machine Learning M Littmann Technische Universität München, 2021 | | 2021 |
| Supporting online material for: Clustering FunFams using sequence embeddings improves EC purity M Littmann, N Bordin, M Heinzinger, K Schütze, C Dallago, C Orengo, ... | | |
