| 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 | 188 | 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, ... International Conference on Learning Representations (ICLR), 2019 | 94 | 2019 |
| A Persistent Weisfeiler-Lehman Procedure for Graph Classification B Rieck*, C Bock*, K Borgwardt International Conference on Machine Learning (ICML), 5448-5458, 2019 | 72 | 2019 |
| Set functions for time series M Horn, M Moor, C Bock, B Rieck, K Borgwardt International Conference on Machine Learning (ICML), 4353-4363, 2020 | 61 | 2020 |
| Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework G Glusman, PW Rose, A Prlić, J Dougherty, JM Duarte, AS Hoffman, ... Genome medicine 9 (1), 1-10, 2017 | 50 | 2017 |
| Association mapping in biomedical time series via statistically significant shapelet mining C Bock, T Gumbsch, M Moor, B Rieck, D Roqueiro, K Borgwardt Bioinformatics 34 (13), i438-i446, 2018 | 26 | 2018 |
| Uncovering the topology of time-varying fmri data using cubical persistence B Rieck*, T Yates*, C Bock, K Borgwardt, G Wolf, N Turk-Browne, ... Advances in Neural Information Processing Systems 33, 2020 | 23 | 2020 |
| Engaging older adults in the visualization of sensor data facilitated by an open platform for connected devices C Bock, G Demiris, Y Choi, T Le, HJ Thompson, A Samuel, D Huang Technology and Health Care 24 (4), 541-550, 2016 | 20 | 2016 |
| Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning C Bock*, M Moor*, CR Jutzeler, K Borgwardt Artificial Neural Networks, 33-71, 2020 | 13 | 2020 |
| A Wasserstein Subsequence Kernel for Time Series C Bock*, M Togninalli*, E Ghisu, T Gumbsch, B Rieck, K Borgwardt International Conference on Data Mining (ICDM), 2019 | 10 | 2019 |
| Biological and functional relevance of CASP predictions T Liu, S Ish‐Shalom, W Torng, A Lafita, C Bock, M Mort, DN Cooper, ... Proteins: Structure, Function, and Bioinformatics 86, 374-386, 2018 | 10 | 2018 |
| Path Imputation Strategies for Signature Models M Moor, M Horn, C Bock, K Borgwardt, B Rieck ICML Workshop on the Art of Learning with Missing Values (Artemiss), 2020 | 6 | 2020 |
| Enhancing statistical power in temporal biomarker discovery through representative shapelet mining T Gumbsch, C Bock, M Moor, B Rieck, K Borgwardt Bioinformatics 36, i840-i848, 2020 | 4 | 2020 |
| Online time series anomaly detection with state space gaussian processes C Bock, FX Aubet, J Gasthaus, A Kan, M Chen, L Callot arXiv preprint arXiv:2201.06763, 2022 | 3 | 2022 |
| Solving Interoperability in Translational Health AM Turner, JC Facelli, M Jaspers, T Wetter, D Pfeifer, LC Gatewood, ... Applied clinical informatics 8 (02), 651-659, 2017 | 2 | 2017 |
| Visualizing Sensor Data through an Open Platform for Connected Devices. C Bock, A Samuel, D Huang, HJ Thompson, G Demiris Studies in Health Technology and Informatics 216, 964-964, 2015 | 2 | 2015 |
| Motifs and Manifolds Statistical and Topological Machine Learning for Characterising and Classifying Biomedical Time Series C Bock ETH Zurich, 2021 | | 2021 |
| 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 | | 2020 |
| Privacy Considerations for the Visualization of Longitudinal Activity and Environmental Data Generated by Smart Home Applications for Older Adults G Demiris, T Le, C Bock, H Thompson, A Samuel, D Huang, ... Gerontologist 55, 563-564, 2015 | | 2015 |
| Topological Methods for fMRI Data B Rieck*, T Yates*, C Bock, K Borgwardt, G Wolf, N Turk-Browne, ... | | |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
