| 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 | 354 | 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 | 147 | 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 | 139 | 2020 |
| A Persistent Weisfeiler-Lehman Procedure for Graph Classification B Rieck*, C Bock*, K Borgwardt International Conference on Machine Learning (ICML), 5448-5458, 2019 | 104 | 2019 |
| 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 | 65 | 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, 1-10, 2017 | 61 | 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 | 30 | 2018 |
| 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 | 27 | 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 | 27 | 2016 |
| 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 | 14 | 2018 |
| 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 | 13 | 2019 |
| 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 | 11 | 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 | 8 | 2022 |
| Thai Le, Hilaire J Thompson, Arjmand Samuel, and Danny Huang. 2016. Engaging older adults in the visualization of sensor data facilitated by an open platform for connected devices C Bock, G Demiris, Y Choi Technology and Health Care 24 (4), 541-550, 2016 | 6 | 2016 |
| 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 | 5 | 2020 |
| 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 | 3 | 2017 |
| Visualizing sensor data through an open platform for connected devices C Bock, T Le, A Samuel, D Huang, HJ Thompson, G Demiris MEDINFO 2015: eHealth-enabled Health, 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 | 1 | 2021 |
| Topological Methods for fMRI Data B Rieck*, T Yates*, C Bock, K Borgwardt, G Wolf, N Turk-Browne, ... | 1 | |
| Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning C Bock, JE Walter, B Rieck, I Strebel, K Rumora, I Schaefer, MJ Zellweger, ... Nature Communications 15 (1), 5034, 2024 | | 2024 |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
