Publikationen von Karsten Borgwardt

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)

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 (accepted) (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)

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

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), S. i247 - i256 (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 (1), 5034 (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, S. 285 - 298 (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)

Pellizzoni, P.; Oliver, C.; Borgwardt, K.: Structure- and function-aware substitution matrices via learnable graph matching. Research in Computational Molecular Biology (RECOMB 2024). Lecture Notes in Computer Science, Vol. 14758, S. 288 - 307 (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) (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), S. 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)

Togninalli, M.; Wang, X.; Kucera, T.; Shrestha, S.; Juliana, P.; Mondal, S.; Pinto, F.; Govindan, V.; Crespo-Herrera, L.; Huerta-Espino, J. et al.; Singh, R. P.; Borgwardt, K.; Poland, J.: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics. Bioinformatics 39 (6), btad336 (2023)

Morris, C.; Lipman, Y.; Maron, H.; Rieck, B.; Kriege, N. M.; Grohe, M.; Fey, M.; Borgwardt, K.: Weisfeiler and Leman go Machine Learning: The Story so far. Journal of Machine Learning Research 24 (333), S. 1 - 59 (2023)

Chen, D.; Fan, B.; Oliver, C.; Borgwardt, K.: Unsupervised Manifold Alignment with Joint Multidimensional Scaling. Eleventh International Conference on Learning Representations (ICLR 2023) (2023)

Chen, D.; Pellizzoni , P.; Borgwardt, K.: Fisher Information Embedding for Node and Graph Learning. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202 (2023)

Kucera, T.; Oliver, C.; Chen, D.; Borgwardt, K.: ProteinShake Building datasets and benchmarks for deep learning on protein structures. Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023)