| Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT J Suez, N Zmora, G Zilberman-Schapira, U Mor, M Dori-Bachash, ... Cell 174 (6), 1406-1423. e16, 2018 | 369 | 2018 |
| Neural persistence: A complexity measure for deep neural networks using algebraic topology B Rieck, M Togninalli, C Bock, M Moor, M Horn, T Gumbsch, K Borgwardt International Conference on Learning Representations, 2019 | 28 | 2019 |
| TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets A Stank, DB Kokh, M Horn, E Sizikova, R Neil, J Panecka, S Richter, ... Nucleic acids research 45 (W1), W325-W330, 2017 | 23 | 2017 |
| Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping M Moor, M Horn, B Rieck, D Roqueiro, K Borgwardt Machine Learning for Healthcare Conference, 2-26, 2019 | 22* | 2019 |
| Early prediction of circulatory failure in the intensive care unit using machine learning SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ... Nature medicine 26 (3), 364-373, 2020 | 21* | 2020 |
| Topological autoencoders M Moor, M Horn, B Rieck, K Borgwardt International Conference on Machine Learning, 7045-7054, 2020 | 13 | 2020 |
| Backbone circularization of Bacillus subtilis family 11 xylanase increases its thermostability and its resistance against aggregation MC Waldhauer, SN Schmitz, C Ahlmann-Eltze, JG Gleixner, CC Schmelas, ... Molecular BioSystems 11 (12), 3231-3243, 2015 | 11 | 2015 |
| Set functions for time series M Horn, M Moor, C Bock, B Rieck, K Borgwardt International Conference on Machine Learning, 4353-4363, 2020 | 3 | 2020 |
| Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series S Ganscha, V Fortuin, M Horn, E Arvaniti, M Claassen bioRxiv, 356477, 2018 | 3 | 2018 |
| Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra C Weis, M Horn, B Rieck, A Cuénod, A Egli, K Borgwardt Bioinformatics 36 (Supplement_1), i30-i38, 2020 | 1 | 2020 |
| Path Imputation Strategies for Signature Models of Irregular Time Series M Moor, M Horn, C Bock, K Borgwardt, B Rieck arXiv preprint arXiv:2005.12359, 2020 | 1 | 2020 |
| Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review. M Moor, B Rieck, M Horn, C Jutzeler, K Borgwardt medRxiv, 2020 | | 2020 |
| Exploring target flexibility for drug design D Kokh, A Stank, M Horn, E Sizikova, R Neil, J Panecka, S Richter, ... ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 254, 2017 | | 2017 |
| BBF RFC 105: The Intein standard-a universal way to modify proteins after translation C Ahlmann-Eltze, C Bunne, M Büscher, J Gleixner, M Horn, A Huhn, ... | | 2015 |
| Challenging Euclidean Topological Autoencoders M Moor, M Horn, K Borgwardt, B Rieck | | |
Bastian RieckMachine Learning & Computational Biology Lab, D-BSSE, ETH ZürichVerified email at bsse.ethz.ch
