| 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 | 717 | 2018 |
| 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 | 179 | 2020 |
| Topological autoencoders M Moor, M Horn, B Rieck, K Borgwardt International Conference on Machine Learning, 7045-7054, 2020 | 96 | 2020 |
| 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 | 85 | 2019 |
| 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 | 69* | 2019 |
| Set functions for time series M Horn, M Moor, C Bock, B Rieck, K Borgwardt International Conference on Machine Learning, 4353-4363, 2020 | 55 | 2020 |
| Early prediction of sepsis in the ICU using machine learning: a systematic review M Moor, B Rieck, M Horn, CR Jutzeler, K Borgwardt Frontiers in medicine 8, 607952, 2021 | 54 | 2021 |
| 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 | 40 | 2017 |
| Topological graph neural networks M Horn, E De Brouwer, M Moor, Y Moreau, B Rieck, K Borgwardt arXiv preprint arXiv:2102.07835, 2021 | 22 | 2021 |
| 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 | 15 | 2015 |
| Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions L O'Bray, M Horn, B Rieck, K Borgwardt arXiv preprint arXiv:2106.01098, 2021 | 10 | 2021 |
| 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 | 10 | 2020 |
| Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... arXiv preprint arXiv:2207.09239, 2022 | 7 | 2022 |
| 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 | 5* | 2020 |
| Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning M Moor, N Bennet, D Plecko, M Horn, B Rieck, N Meinshausen, ... arXiv preprint arXiv:2107.05230, 2021 | 3 | 2021 |
| Bridging the gap to real-world object-centric learning M Seitzer, M Horn, A Zadaianchuk, D Zietlow, T Xiao, CJ Simon-Gabriel, ... arXiv preprint arXiv:2209.14860, 2022 | 2 | 2022 |
| Challenging euclidean topological autoencoders M Moor, M Horn, K Borgwardt, B Rieck TDA {\&} Beyond, 2020 | 2 | 2020 |
| Pathologies in priors and inference for Bayesian transformers T Cinquin, A Immer, M Horn, V Fortuin arXiv preprint arXiv:2110.04020, 2021 | 1 | 2021 |
| Translational equivariance in kernelizable attention M Horn, K Shridhar, E Groenewald, PFM Baumann arXiv preprint arXiv:2102.07680, 2021 | 1 | 2021 |
| 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 | 1 | 2018 |
Bastian RieckAIDOS Lab, Institute of AI for Health, Helmholtz Zentrum MünchenVerified email at helmholtz-muenchen.de
