| Wasserstein weisfeiler-lehman graph kernels M Togninalli, E Ghisu, F Llinares-López, B Rieck, K Borgwardt Advances in neural information processing systems 32, 2019 | 141 | 2019 |
| Graph kernels: State-of-the-art and future challenges K Borgwardt, E Ghisu, F Llinares-López, L O’Bray, B Rieck Foundations and Trends® in Machine Learning 13 (5-6), 531-712, 2020 | 58 | 2020 |
| graphkernels: R and Python packages for graph comparison M Sugiyama, ME Ghisu, F Llinares-López, K Borgwardt Bioinformatics 34 (3), 530-532, 2018 | 57 | 2018 |
| Aberrant working memory processing in major depression: evidence from multivoxel pattern classification M Gärtner, ME Ghisu, M Scheidegger, L Bönke, Y Fan, A Stippl, ... Neuropsychopharmacology 43 (9), 1972-1979, 2018 | 26 | 2018 |
| A wasserstein subsequence kernel for time series C Bock, M Togninalli, E Ghisu, T Gumbsch, B Rieck, K Borgwardt 2019 IEEE International Conference on Data Mining (ICDM), 964-969, 2019 | 9 | 2019 |
| Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy M Gärtner, E Ghisu, AL Herrera-Melendez, M Koslowski, S Aust, P Asbach, ... Experimental neurology 335, 113505, 2021 | 7 | 2021 |
| Towards MRI data analysis via learning on graphs E Ghisu ETH Zurich, 2020 | | 2020 |
