Publications

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 (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)

Gimpel, A. L.; Fan, B.; Chen, D.; Wölfle, L. O. D.; Horn, M.; Meng-Papaxanthos, L.; Antkowiak, P. L.; Stark, W. J.; Christen, B.; Borgwardt, K. et al.; Grass, R. N.: Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning. Nature Communications 16, 9187 (2025)

Huber, L.; Kucera, T.; Hoellerer, S.; Borgwardt, K.; Panke, S.; Jeschek, M.: Data-driven Protease Engineering by DNA-Recording and Epistasis-aware Machine Learning. Nature Communications 16, 5466 (2025)

Chen, D.; Krimmel, M.; Borgwardt, K.: Flatten Graphs as Sequences: Transformers are Scalable Graph Generators. Conference on Neural Information Processing Systems (NeurIPS 2025) (2025)

Chen, D.; Schulz, T.; Borgwardt, K.: Learning Long Range Dependencies on Graphs via Random Walks. 13th International Conference on Learning Representations (ICLR 2025) (2025)

Corvelo Benz, N.; Miranda, L.; Chen, D.; Sattler, J.; Borgwardt, K.: Detecting antimicrobial resistance through MALDI-TOF mass spectrometry with statistical guarantees using conformal prediction. 29th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2025) (2025)

Pellizzoni, P.; Schulz, T.; Chen, D.; Borgwardt, K.: Graph Neural Networks Can (Often) Count Substructures. 13th International Conference on Learning Representations (ICLR 2025) (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)