Publications

Lyu, X.; Fan, B.; Hüser, M.; Hartout, P.; Gumbsch, T.; Faltys, M.; Merz, T. M.; Rätsch, G.; Borgwardt, K.: An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinformatics 40 (Suppl. 1), pp. i247 - i256 (2024)

Adamer, M. F.; Brüningk, S. C.; Chen, D.; Borgwardt, K.: Biomarker identification by interpretable maximum mean discrepancy. Bioinformatics 40 (Suppl. 1), pp. i501 - i510 (2024)

Bock, C.; Walter, J. E.; Rieck, B.; Strebel, I.; Rumora, K.; Schaefer, I.; Zellweger, M. J.; Borgwardt, K.; Müller, C.: Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning. Nature Communications 15, 5034 (2024)

Cervia-Hasler, C.; Brüningk, S. C.; Hoch, T.; Fan, B.; Muzio, G.; Thompson, R. C.; Ceglarek, L.; Meledin, R.; Westermann, P.; Emmenegger, M. et al.; Taeschler, P.; Zurbuchen, Y.; Pons, M.; Menges, D.; Ballouz, T.; Cervia-Hasler, S.; Adamo, S.; Merad, M.; Charney, A. W.; Puhan, M.; Brodin, P.; Nilsson, J.; Aguzzi, A.; Raeber, M. E.; Messner, C. B.; Beckmann, N. D.; Borgwardt, K.; Boyman, O.: Persistent complement dysregulation with signs of thromboinflammation in active Long Covid. Science 383 (6680), eadg7942 (2024)

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.: DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks. Stem Cell Reports 19, pp. 285 - 298 (2024)

Pellizzoni, P.; Oliver, C.; Borgwardt, K.: Structure- and function-aware substitution matrices via learnable graph matching. In: Research in Computational Molecular Biology (RECOMB 2024). Lecture Notes in Computer Science, Vol. 14758, pp. 288 - 307. 28th Annual International Conference on Research in Computational Molecular Biology (RECOMB), Cambridge, MA, April 29, 2024 - May 02, 2024. (2024)

Visonà, G.; Duroux, D.; Miranda, L.; Sükei, E.; Li, Y.; Borgwardt, K.; Oliver, C.: Multimodal learning in clinical proteomics: Enhancing antimicrobial resistance prediction models with chemical information. Bioinformatics 39 (12), btad717 (2023)

Moor, M.; Bennett, N.; Plečko, D.; Horn, M.; Rieck, B.; Meinshausen, N.; Bühlmann, P.; Borgwardt, K.: Predicting sepsis using deep learning across international sites: a retrospective development and validation study. eClinicalMedicine 62, 102124 (2023)

Pellizzoni, P.; Muzio, G.; Borgwardt, K.: Higher-order genetic interaction discovery with network-based biological priors. Bioinformatics 39 (Suppl. 1), pp. 523 - 533 (2023)

Muzio, G.; O’Bray, L.; Meng-Papaxanthos, L.; Klatt, J.; Borgwardt, K.: networkGWAS: A network-based approach to discover genetic associations. Bioinformatics 39 (6), btad370 (2023)

Chen, D.; Hartout, P.; Pellizzoni, P.; Oliver, C.; Borgwardt, K.: Endowing protein language models with structural knowledge. arXiv: Condensed Matter-Materials Science (2024)

Pellizzoni, P.; Schulz, T.; Chen, D.; Borgwardt, K. M.: On the expressivity and sample complexity of node-individualized graph neural networks. Conference on Neural Information Processing Systems (NeurIPS) (2024)

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

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)

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