Found 19 results
Filters: Author is Heinzinger, Michael  [Clear All Filters]
Littmann M, Bordin N, Heinzinger M, Schütze K, Dallago C, Orengo C, Rost B. Clustering FunFams using sequence embeddings improves EC purity. Bioinformatics. 2021 .
Littmann M, Heinzinger M, Dallago C, Olenyi T, Rost B. Embeddings from deep learning transfer GO annotations beyond homology. Sci Rep. 2021 ;11(1):1160.
Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet. 2021 .
Dallago C, Schütze K, Heinzinger M, Olenyi T, Littmann M, Lu AX, Yang KK, Min S, Yoon S, Morton JT, et al. Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Curr Protoc. 2021 ;1(5):e113.
Bernhofer M, Dallago C, Karl T, Satagopam V, Heinzinger M, Littmann M, Olenyi T, Qiu J, Schütze K, Yachdav G, et al. PredictProtein - Predicting Protein Structure and Function for 29 Years. Nucleic Acids Res. 2021 ;49(W1):W535-W540.
Littmann M, Heinzinger M, Dallago C, Weissenow K, Rost B. Protein embeddings and deep learning predict binding residues for various ligand classes. Sci Rep. 2021 ;11(1):23916.
Heinzinger M, Dallago C, Rost B. Protein matchmaking through representation learning. Cell Syst. 2021 ;12(10):948-950.
Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Yu W, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, et al. ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing. IEEE Trans Pattern Anal Mach Intell. 2021 ;PP.