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)

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, 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 (Supplement_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)

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)

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)

Pellizzoni, P.; Borgwardt, K.: FASM and FAST-YB: Significant Pattern Mining with False Discovery Rate Control. 2023 IEEE International Conference on Data Mining (ICDM), pp. 1265 - 1270 (2023)

Adamer, M. F.; Roellin, E.; Bourguignon, L.; Borgwardt, K.: SIMBSIG: similarity search and clustering for biobank-scale data. Bioinformatics 39 (1), btac829 (2022)

Adamer, M. F.; Brüningk, S. C.; Tejada-Arranz, A.; Estermann, F.; Basler, M.; Borgwardt, K.: reComBat: batch-effect removal in large-scale multi-source gene-expression data integration. Bioinformatics Advances 2 (1), vbac071 (2022)

Brüningk, S. C.; Klatt, J.; Stange, M.; Mari, A.; Brunner, M.; Roloff, T.-C.; Seth-Smith, H. M. B.; Schweitzer, M.; Leuzinger, K.; Søgaard, K. K. et al.; Albertos Torres, D.; Gensch, A.; Schlotterbeck, A.-K.; Nickel, C. H.; Ritz, N.; Heininger, U.; Bielicki, J.; Rentsch, K.; Fuchs, S.; Bingisser, R.; Siegemund, M.; Pargger, H.; Ciardo, D.; Dubuis, O.; Buser, A.; Tschudin-Sutter, S.; Battegay, M.; Schneider-Sliwa, R.; Borgwardt, K. M.; Hirsch, H. H.; Egli, A.: Determinants of SARS-CoV-2 transmission to guide vaccination strategy in an urban area. Virus Evolution 8 (1), veac002 (2022)

Chen, D.; O’Bray, L.; Borgwardt, K.: Structure-Aware Transformer for Graph Representation Learning. Proceedings of the 39th International Conference on Machine Learning (ICML 2022) 162, pp. 3469 - 3489 (2022)

Fan, B.; Klatt, J.; Moor, M. M.; Daniels, L. A.; Swiss Pediatric Sepsis, S.; Agyeman, P. K. A.; Berger, C.; Giannoni, E.; Stocker, M.; Posfay-Barbe, K. M. et al.; Heininger, U.; Bernhard-Stirnemann, S.; Niederer-Loher, A.; Kahlert, C. R.; Natalucci, G.; Relly, C.; Riedel, T.; Aebi, C.; Schlapbach, L. J.; Sanchez-Pinto, L. N.; Agyeman, P. K. A.; Schlapbach, L. J.; Borgwardt, K.: Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients. Bioinformatics 38 (Supplement_1), pp. i101 - i108 (2022)

O'Bray*, L.; Horn*, M.; Rieck*, B.; Borgwardt*, K.; (* = equal contribution): Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. Tenth International Conference on Learning Representations (ICLR 2022) (2022)

Weis, C.; Cuénod, A.; Rieck, B.; Dubuis, O.; Graf, S.; Lang, C.; Oberle, M.; Brackmann, M.; Søgaard, K. K.; Osthoff, M. et al.; Borgwardt*, K.; Egli*, A.; (* = equal contribution): Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nature Medicine 28 (1), pp. 164 - 174 (2022)

O'Bray, L.; Rieck, B.; Borgwardt, K.: Filtration Curves for Graph Representation. KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1267 - 1275 (2021)