Publications

Found 9 results
Filters: Author is Littmann, Maria  [Clear All Filters]
2022
Heinzinger M, Littmann M, Sillitoe I, Bordin N, Orengo C, Rost B. Contrastive learning on protein embeddings enlightens midnight zone. NAR Genom Bioinform. 2022 ;4(2):lqac043.
2021
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.
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.
2019
Littmann M, Goldberg T, Seitz S, Boden M, Rost B. Correction to: Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics. 2019 ;20(1):727.
Littmann M, Goldberg T, Seitz S, Boden M, Rost B. Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics. 2019 ;20(1):205.
Scheibenreif L, Littmann M, Orengo C, Rost B. FunFam protein families improve residue level molecular function prediction. BMC Bioinformatics. 2019 ;20(1):400.