Publications of K Borgwardt

Gärtner, M.; Ghisu, M.; Scheidegger, M.; Bönke, L.; Fan, Y.; Stippl, A.; Herrera-Melendez, A.; Metz, S.; Winnebeck, E.; Fissler, M. et al.; Henning, A.; Bajbouj, M.; Borgwardt, K.; Barnhofer, T.; Grimm, S.: Aberrant working memory processing in major depression: evidence from multivoxel pattern classification. Neuropsychopharmacology 43 (9), pp. 1972 - 1979 (2018)

Karaletsos, T.; Stegle, O.; Dreyer, C.; Winn, J.; Borgwardt, K.: ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections. Bioinformatics 28 (7), pp. 1001 - 1008 (2012)

Gretton, A.; Borgwardt, K.; Rasch, M.; Schölkopf, B.; Smola, A.: A Kernel Two-Sample Test. Journal of Machine Learning Research 13, pp. 723 - 773 (2012)

Song, L.; Smola, A.; Gretton, A.; Bedo, J.; Borgwardt, K.: Feature Selection via Dependence Maximization. Journal of Machine Learning Research 13, pp. 1393 - 1434 (2012)

Becker, C.; Hagmann, J.; Müller, J.; Koenig, D.; Stegle, O.; Borgwardt, K.; Weigel, D.: Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480 (7376), pp. 245 - 249 (2011)

Cao, J.; Schneeberger, K.; Ossowski , S.; Günther, T.; Bender , S.; Fitz, J.; Koenig, D.; Lanz, C.; Stegle, O.; Lippert, C. et al.; Wang, X.; Ott, F.; Müller, J.; Alonso-Blanco, C.; Borgwardt, K.; Schmid, K.; Weigel, D.: Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nature Genetics 43 (10), pp. 956 - 963 (2011)

Shervashidze, N.; Schweitzer, P.; van Leeuwen , E.; Mehlhorn, K.; Borgwardt, M.: Weisfeiler-Lehman Graph Kernels. The Journal of Machine Learning Research 12, pp. 2539 - 2561 (2011)

Kam-Thong, T.; Czamara, D.; Tsuda, K.; Borgwardt, K.; Lewis, C.; Erhardt-Lehmann , A.; Hemmer, B.; Rieckmann, P.; Daake, M.; Weber, F. et al.; Wolf, C.; Ziegler, A.; Pütz, B.; Holsboer , F.; Schölkopf, B.; Müller-Myhsok, B.: EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. European Journal of Human Genetics 19 (4), pp. 465 - 471 (2011)

Camps-Valls, G.; Shervashidze, N.; Borgwardt, K.: Spatio-Spectral Remote Sensing Image Classification With Graph Kernels. IEEE Geoscience and Remote Sensing Letters 7 (4), pp. 741 - 745 (2010)

Thoma, M.; Cheng, H.; Gretton, A.; Han, J.; Kriegel, H.-P.; Smola, A.; Song, L.; Yu, P.; Yan, X.; Borgwardt, K.: Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining 3 (5), pp. 302 - 318 (2010)

Stegle, O.; Denby, K.; Cooke, E.; Wild, D.; Ghahramani, Z.; Borgwardt, K.: A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. Journal of Computational Biology 17 (3), pp. 355 - 367 (2010)

Stegle, O.; Drewe, P.; Bohnert, R.; Borgwardt, K.; Rätsch, G.: Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts. Nature Precedings 2010, pp. 1 - 11 (2010)

Lippert, C.; Ghahramani, Z.; Borgwardt, K.: Gene function prediction from synthetic lethality networks via ranking on demand. Bioinformatics 26 (7), pp. 912 - 918 (2010)

Vishwanathan, S.; Schraudolph, N.; Kondor, R.; Borgwardt, K.: Graph Kernels. Journal of Machine Learning Research 11, pp. 1201 - 1242 (2010)

Borgwardt, K.: Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model. Bioessays 30 (8), pp. 707 - 710 (2008)

Vishwanathan, S.; Borgwardt, K.; Guttman, O.; Smola, A.: Kernel extrapolation. Neurocomputing 69 (7-9), pp. 721 - 729 (2006)

Borgwardt, K.; Ong, C.; Schönauer, S.; Vishwanathan , S.; Smola, A.; Kriegel, H.-P.: Protein function prediction via graph kernels. Bioinformatics 21 (Supplement 1), pp. i47 - i56 (2005)

Borgwardt, K.: Kernel Methods in Bioinformatics. In: Handbook of Statistical Bioinformatics, pp. 317 - 334 (Eds. Lu, H.-S.; Schölkopf, B.; Zhao, H.). Springer, Berlin, Germany (2011)

Gretton, A.; Smola, A.; Huang, J.; Schmittfull, M.; Borgwardt, K.; Schölkopf, B.: Covariate Shift by Kernel Mean Matching. In: Dataset Shift in Machine Learning, 8, pp. 131 - 160 (Eds. Quiñonero-Candela, J.; Sugiyama, M.; Schwaighofer, A.; Lawrence, N.). MIT Press, Cambridge, MA, USA (2009)

Stegle, O.; Lippert, C.; Mooij, J.; Lawrence, N.; Borgwardt, K.: Efficient inference in matrix-variate Gaussian models with iid observation noise. In: Advances in Neural Information Processing Systems 24, pp. 630 - 638 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)