Publications of Karsten Borgwardt

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 (Suppl. 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)

Moor, M.; Rieck, B.; Horn, M.; Jutzeler, C. R.; Borgwardt, K.: Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine 8, 607952 (2021)

Gumbsch, T.; Borgwardt, K.: Ethnicity-based bias in clinical severity scores. The Lancet Digital Health 3 (4), pp. 209 - 210 (2021)

Avican, K.; Aldahdooh, J.; Togninalli, M.; Mahmud, A. K. M. F.; Tang, J.; Borgwardt, K. M.; Rhen, M.; Fällman, M.: RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection. Nature Communications 12 (1), 3282 (2021)

Gärtner, M.; Ghisu, E.; Herrera-Melendez, A. L.; Koslowski, M.; Aust, S.; Asbach, P.; Otte, C.; Regen, F.; Heuser, I.; Borgwardt, K. et al.; Grimm, S.; Bajbouj, M.: Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy. Experimental Neurology 335, 113505 (2021)

Gumpinger, A. C.; Rieck, B.; Grimm, D. G.; International Headache Genetics, C.; Borgwardt, K.: Network-guided search for genetic heterogeneity between gene pairs. Bioinformatics 37 (1), pp. 57 - 65 (2021)

Muzio*, G.; O’Bray*, L.; Borgwardt*, K.; (* = equal contribution): Biological network analysis with deep learning. Briefings in Bioinformatics 22 (2), pp. 1515 - 1530 (2021)

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)