Publications

Gumbsch, T.; Bock, C.; Moor, M.; Rieck, B.; Borgwardt, K.: Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. Bioinformatics 36 (Supplement_2), pp. i840 - i848 (2020)

Weis, C. V.; Jutzeler, C. R.; Borgwardt, K.: Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clinical Microbiology and Infection 26 (10), pp. 1310 - 1317 (2020)

Togninalli, M.; Seren, Ü.; Freudenthal, J. A.; Monroe, J. G.; Meng, D.; Nordborg, M.; Weigel, D.; Borgwardt, K.; Korte, A.; Grimm, D. G.: AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana. Nucleic Acids Research 48 (D1), pp. D1063 - D1068 (2020)

Borgwardt, K.; Ghisu, E.; Llinares-López, F.; O’Bray, L.; Rieck, B.: Graph Kernels: State-of-the-Art and Future Challenges. Foundations and Trends® in Machine Learning 13 (5-6), pp. 531 - 712 (2020)

Egli, A.; Battegay, M.; Büchler, A. C.; Bühlmann, P.; Calandra, T.; Eckert, P.; Furrer, H.; Greub, G.; Jakob, S. M.; Kaiser, L. et al.; Leib, S. L.; Marsch, S.; Meinshausen, N.; Pagani, J.-L.; Pugin, J.; Rätsch, G.; Schrenzel, J.; Schüpbach, R.; Siegemund, M.; Zamboni, N.; Zbinden, R.; Zinkernagel, A.; Borgwardt, K.: SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. Studies in Health Technology and Informatics 270, pp. 1163 - 1167 (2020)

Gumpinger, A. C.; Lage, K.; Horn, H.; Borgwardt, K.: Prediction of cancer driver genes through network-based moment propagation of mutation scores. Bioinformatics 36 (Supplement_1), pp. i508 - i515 (2020)

He, X.; Gumbsch, T.; Roqueiro, D.; Borgwardt, K.: Kernel conditional clustering and kernel conditional semi-supervised learning. Knowledge and Information Systems 62 (3), pp. 899 - 925 (2020)

Höllerer, S.; Papaxanthos, L.; Gumpinger, A. C.; Fischer, K.; Beisel, C.; Borgwardt, K.; Benenson, Y.; Jeschek, M.: Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nature Communications 11, 3551 (2020)

Horn, M.; Moor, M.; Bock, C.; Rieck, B.; Borgwardt, K.: Set Functions for Time Series. Proceedings of the 37th International Conference on Machine Learning, PMLR (2020)

Hyland, S. L.; Faltys, M.; Hüser, M.; Lyu, X.; Gumbsch, T.; Esteban, C.; Bock, C.; Horn, M.; Moor, M.; Rieck, B. et al.; Zimmermann, M.; Bodenham, D.; Borgwardt, K.; Rätsch, G.; Merz, T. M.: Early prediction of circulatory failure in the intensive care unit using machine learning. Nature Medicine 26 (3), pp. 364 - 373 (2020)

Jutzeler, C. R.; Bourguignon, L.; Weis, C. V.; Tong, B.; Wong, C.; Rieck, B.; Pargger, H.; Tschudin-Sutter, S.; Egli, A.; Borgwardt, K. et al.; Walter, M.: Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis. Travel Medicine and Infectious Disease 37, 101825 (2020)

Moor, M.; Horn, M.; Rieck, B.; Borgwardt, K.: Topological Autoencoders. Proceedings of the 37th International Conference on Machine Learning, PMLR 119, pp. 7045 - 7054 (2020)

Rieck, B.; Yates, T.; Bock, C.; Borgwardt, K.; Wolf, G.; Turk-Browne, N.; Krishnaswamy, S.: Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. Advances in Neural Information Processing Systems (NeurIPS 2020) 33, pp. 6900 - 6912 (2020)

Weis, C.; Horn, M.; Rieck, B.; Cuénod, A.; Egli, A.; Borgwardt, K.: Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Bioinformatics 36 (Supplement_1), pp. i30 - i38 (2020)

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. (2020)