Publications

Journal Articles, Magazine Article & Conference Contributions

1.
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)
2.
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)
3.
Pellizzoni, P.; Muzio, G.; Borgwardt, K.: Higher-order genetic interaction discovery with network-based biological priors. Bioinformatics 39 (Supplement_1), pp. 523 - 533 (2023)
4.
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)
5.
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)
6.
Chen, D.; Fan, B.; Oliver, C.; Borgwardt, K.: Unsupervised Manifold Alignment with Joint Multidimensional Scaling. Eleventh International Conference on Learning Representations (ICLR 2023) (2023)
7.
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)
8.
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)
9.
Adamer, M. F.; Roellin, E.; Bourguignon, L.; Borgwardt, K.: SIMBSIG: similarity search and clustering for biobank-scale data. Bioinformatics 39 (1), btac829 (2022)
10.
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)
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Book Chapter & Preprint

1.
Oliver, C.; Chen, D.; Mallet, V.; Philippopoulos, P.; Borgwardt, K.: Approximate Network Motif Mining Via Graph Learning. (2022)
2.
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. (2022)
3.
Weis, C.; Rieck, B.; Balzer, S.; Cuénod, A.; Egli, A.; Borgwardt, K.: Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification. (2022)
4.
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. (2021)
5.
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. (2021)
6.
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)
7.
Moor, M.; Horn, M.; Bock, C.; Borgwardt, K.; Rieck, B.: Path Imputation Strategies for Signature Models of Irregular Time Series. (2020)
8.
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)
9.
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)
10.
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)
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