Gluonts: Probabilistic and neural time series modeling in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 362* | 2020 |
High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus Advances in neural information processing systems 32, 2019 | 234 | 2019 |
Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 222 | 2020 |
Deep learning for time series forecasting: Tutorial and literature survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys 55 (6), 1-36, 2022 | 166 | 2022 |
An integrated workflow for crosslinking mass spectrometry ML Mendes, L Fischer, ZA Chen, M Barbon, FJ O'Reilly, SH Giese, ... Molecular systems biology 15 (9), e8994, 2019 | 159 | 2019 |
Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240 6, 2020 | 123 | 2020 |
Normalizing kalman filters for multivariate time series analysis E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ... Advances in Neural Information Processing Systems 33, 2995-3007, 2020 | 120 | 2020 |
Protein tertiary structure by crosslinking/mass spectrometry M Schneider, A Belsom, J Rappsilber Trends in biochemical sciences 43 (3), 157-169, 2018 | 111 | 2018 |
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology A Belsom, M Schneider, L Fischer, O Brock, J Rappsilber Molecular & Cellular Proteomics 15 (3), 1105-1116, 2016 | 110 | 2016 |
X‐ray vs. NMR structures as templates for computational protein design M Schneider, X Fu, AE Keating Proteins: Structure, Function, and Bioinformatics 77 (1), 97-110, 2009 | 69 | 2009 |
In situ structural restraints from cross-linking mass spectrometry in human mitochondria PSJ Ryl, M Bohlke-Schneider, S Lenz, L Fischer, L Budzinski, M Stuiver, ... Journal of Proteome Research 19 (1), 327-336, 2019 | 60 | 2019 |
Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 49 | 2024 |
PSA-GAN: Progressive self attention GANs for synthetic time series P Jeha, M Bohlke-Schneider, P Mercado, S Kapoor, RS Nirwan, ... The Tenth International Conference on Learning Representations, 2022 | 42* | 2022 |
Combining physicochemical and evolutionary information for protein contact prediction M Schneider, O Brock PloS one 9 (10), e108438, 2014 | 40 | 2014 |
EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction K Stahl, M Schneider, O Brock BMC bioinformatics 18, 1-11, 2017 | 38 | 2017 |
Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11 M Schneider, A Belsom, J Rappsilber, O Brock Proteins: Structure, Function, and Bioinformatics 84, 152-163, 2016 | 27 | 2016 |
RBO Aleph: leveraging novel information sources for protein structure prediction M Mabrouk, I Putz, T Werner, M Schneider, M Neeb, P Bartels, O Brock Nucleic acids research 43 (W1), W343-W348, 2015 | 22 | 2015 |
Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting M Kollovieh, AF Ansari, M Bohlke-Schneider, J Zschiegner, H Wang, ... Advances in Neural Information Processing Systems 36, 2024 | 20 | 2024 |
Blind evaluation of hybrid protein structure analysis methods based on cross-linking A Belsom, M Schneider, O Brock, J Rappsilber Trends in biochemical sciences 41 (7), 564-567, 2016 | 20 | 2016 |
The structure of active opsin as a basis for identification of GPCR agonists by dynamic homology modelling and virtual screening assays M Schneider, S Wolf, J Schlitter, K Gerwert FEBS letters 585 (22), 3587-3592, 2011 | 18 | 2011 |