Maria Littmann
Title
Cited by
Cited by
Year
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), 1-13, 2017
162017
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
132020
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
82018
FunFam protein families improve residue level molecular function prediction
L Scheibenreif, M Littmann, C Orengo, B Rost
BMC bioinformatics 20 (1), 1-9, 2019
52019
Embeddings from deep learning transfer GO annotations beyond homology
M Littmann, M Heinzinger, C Dallago, T Olenyi, B Rost
Scientific reports 11 (1), 1-14, 2021
32021
Detailed prediction of protein sub-nuclear localization
M Littmann, T Goldberg, S Seitz, M Bodén, B Rost
BMC bioinformatics 20 (1), 1-15, 2019
32019
PredictProtein-Predicting Protein Structure and Function for 29 Years
M Bernhofer, C Dallago, T Karl, V Satagopam, M Heinzinger, M Littmann, ...
bioRxiv, 2021
2021
Clustering FunFams using sequence embeddings improves EC purity
M Littmann, N Bordin, M Heinzinger, C Orengo, B Rost
bioRxiv, 2021
2021
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Articles 1–8