Publications

Pichl, T.; Miranda, L.; Warkotsch, M. T.; Wantia, N.; Borgwardt, K.; Sattler, J.: Early detection of ampicillin susceptibility in Enterococcus faecium with MALDI-TOF MS and machine learning. Journal of Global Antimicrobial Resistance 48, pp. 145 - 150 (2026)

Krimmel, M.; Hartout, P.; Borgwardt, K.; Chen, D.: PolyGraph Discrepancy: A Classifier-Based Metric for Graph Generation. arXiv (2026)

Weiss, C. A. M.; Brown, L. A.; Miranda, L.; Pellizzoni, P.; Steigerwald, S.; Remmert, K.; Hernandez, J. M.; Borgwardt, K.; Kleiner, D. E.; Ben-Moshe, S. et al.; Rosenberger, F. A.; Porat-Shliom, N.; Mann, M.: Single-cell spatial proteomics maps human liver zonation patterns and their vulnerability to disruption in tissue architecture. Nature Metabolism 8, pp. 741 - 756 (2026)

Stan-Bernhardt, A.; Pellizzoni, P.; Borgwardt, K.; Ochsenfeld, C.: Automated Discovery of Reactive Events via Hypergraph Mining of Ab Initio Atomistic Simulations. Journal of Chemical Theory and Computation 22 (4), pp. 1674 - 1686 (2026)

Krimmel, M.; Wiens, J.; Borgwardt, K.; Chen, D.: Fast Graph Generation via Autoregressive Noisy Filtration Modeling. Transactions on Machine Learning Research (2026)

Kaech, B.; Wyss, L.; Borgwardt, K.; Grassow, G.: Refine Drugs, Don’t Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery. 14th International Conference on Learning Representations (ICLR 2026) (2026)

Pellizzoni, P.; Schulz, T. H.; Borgwardt, K.: Gelato: Graph Edit Distance via Autoregressive Neural Combinatorial Optimization. 14th International Conference on Learning Representations (ICLR 2026) (2026)

Corvelo Benz, N.; Miranda, L.; Chen, D.; Sattler, J.; Borgwardt, K.: Conformal Prediction with Knowledge Graphs for Reliable Antimicrobial Resistance Detection with MALDI-TOF Mass Spectra. Journal of Computational Biology 33 (1), pp. 19 - 35 (2026)

Sattler, J.; Oster, Y.; Seth Smith, H. M. B.; Gutlin, Y.; Michael-Gayego, A.; Reshef, D.; Temper, V.; Borgwardt, K.; Egli, A.; Strahilevitz, J. et al.; Moran-Gilad, J.: Circulation of polyclonal OXA-244-producing Escherichia coli lineages in Jerusalem, Israel. JAC-Antimicrobial Resistance 7 (6), dlaf210 (2025)

Chen, D.; Hartout, P.; Pellizzoni, P.; Oliver, C.; Borgwardt, K.: Endowing protein language models with structural knowledge. Bioinformatics 41 (11), btaf582 (2025)

Bock, C.; Moor, M.; Jutzeler, C. R.; Borgwardt, K.: Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning. In: Artificial Neural Networks, Vol. 2190, pp. 33 - 71 (Ed. Cartwright, H.) (2020)

Llinares-​López, F.; Borgwardt, K.: Machine Learning for Biomarker Discovery: Significant Pattern Mining. In: Analyzing Network Data in Biology and Medicine, pp. 313 - 368 (Ed. Pržulj, N.). Cambridge University Press (2019)

Gumpinger, A. C.; Roqueiro, D.; Grimm, D. G.; Borgwardt, K. M.: Methods and Tools in Genome-wide Association Studies. In: Computational Cell Biology, Vol. 1819, pp. 93 - 136 (Eds. von Stechow, L.; Santos Delgado, A.). Springer New York, New York, NY (2018)

He, X.; Li, L.; Roqueiro, D.; Borgwardt, K.: Multi-view Spectral Clustering on Conflicting Views. In: Machine Learning and Knowledge Discovery in Databases, Vol. 10535, pp. 826 - 842 (Eds. Ceci, M.; Hollmén, J.; Todorovski, L.; Vens, C.; Džeroski, S.). Springer International Publishing, Cham (2017)

Sugiyama, M.; Azencott, C.-A.; Grimm, D.; Kawahara, Y.; Borgwardt, K.: Multi-Task Feature Selection on Multiple Networks via Maximum Flows. In: Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pp. 199 - 207 (2014)

Feragen, A.; Petersen, J.; Grimm, D.; Dirksen, A.; Pedersen, J. H.; Borgwardt, K.; de Bruijne, M.: Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry. In: Information Processing in Medical Imaging, Vol. 7917, pp. 171 - 183 (Eds. Gee, J. C.; Joshi, S.; Pohl, K. M.; Wells, W. M.; Zöllei, L. et al.) (2013)

Borgwardt, K. M.: Kernel Methods in Bioinformatics. In: Handbook of Statistical Bioinformatics, pp. 317 - 334 (Eds. Lu, H. H.-S.; Schölkopf, B.; Zhao, H.). Springer, Berlin, Heidelberg (2011)

Gretton, A.; Smola, A.; Huang, J.; Schmittfull, M.; Borgwardt, K.; Schölkopf, B.: Covariate shift by kernel mean matching. In: Dataset shift in machine learning, Vol. 3, p. 5 (Eds. Quiñonero-​Candela, J.; Sugiyama, M.; Schwaighofer, A.; Lawrence, N. D.). MIT Press, Cambridge (2008)

Muzio, G.; Adamer, M.; Fernandez, L.; Borgwardt, K.; Avican, K.: Bacterial protein function prediction via multimodal deep learning. bioRxiv: the preprint server for biology (2024)

Oliver, C.; Chen, D.; Mallet, V.; Philippopoulos, P.; Borgwardt, K.: Approximate Network Motif Mining Via Graph Learning. arXiv (2022)

Weis, C.; Rieck, B.; Balzer, S.; Cuénod, A.; Egli, A.; Borgwardt, K.: Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification. bioRxiv: the preprint server for biology (2022)

Hornauer, P.; Prack, G.; Anastasi, N.; Ronchi, S.; Kim, T.; Donner, C.; Fiscella, M.; Borgwardt, K.; Taylor, V.; Jagasia, R. et al.; Roqueiro, D.; Hierlemann, A.; Schröter, M.: Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation. bioRxiv: the preprint server for biology (2022)

Moor, M.; Bennet, N.; Plecko, D.; Horn, M.; Rieck, B.; Meinshausen, N.; Bühlmann, P.; Borgwardt, K.: Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. arXiv (2021)

Moor, M.; Horn, M.; Bock, C.; Borgwardt, K.; Rieck, B.: Path Imputation Strategies for Signature Models of Irregular Time Series. arXiv (2020)