| The SHOGUN machine learning toolbox S Sonnenburg, G Rätsch, S Henschel, C Widmer, J Behr, A Zien, F Bona, ... The Journal of Machine Learning Research 11, 1799-1802, 2010 | 419 | 2010 |
| Stress‐induced changes in the Arabidopsis thaliana transcriptome analyzed using whole‐genome tiling arrays G Zeller, SR Henz, CK Widmer, T Sachsenberg, G Rätsch, D Weigel, ... The Plant Journal 58 (6), 1068-1082, 2009 | 377 | 2009 |
| A spatial and temporal map of C. elegans gene expression WC Spencer, G Zeller, JD Watson, SR Henz, KL Watkins, RD McWhirter, ... Genome research 21 (2), 325-341, 2011 | 354 | 2011 |
| Prediction of potent shRNAs with a sequential classification algorithm R Pelossof, L Fairchild, CH Huang, C Widmer, VT Sreedharan, N Sinha, ... Nature biotechnology 35 (4), 350-353, 2017 | 212 | 2017 |
| An empirical analysis of domain adaptation algorithms for genomic sequence analysis G Schweikert, G Rätsch, C Widmer, B Schölkopf Advances in neural information processing systems 21, 2008 | 203 | 2008 |
| Determinants of robustness in spindle assembly checkpoint signalling S Heinrich, EM Geissen, J Kamenz, S Trautmann, C Widmer, P Drewe, ... Nature cell biology 15 (11), 1328-1339, 2013 | 138 | 2013 |
| At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana S Laubinger, G Zeller, SR Henz, T Sachsenberg, CK Widmer, N Naouar, ... Genome biology 9 (7), R112, 2008 | 119 | 2008 |
| Linear mixed model for heritability estimation that explicitly addresses environmental variation D Heckerman, D Gurdasani, C Kadie, C Pomilla, T Carstensen, H Martin, ... Proceedings of the National Academy of Sciences 113 (27), 7377-7382, 2016 | 96 | 2016 |
| Further improvements to linear mixed models for genome-wide association studies C Widmer, C Lippert, O Weissbrod, N Fusi, C Kadie, R Davidson, ... Scientific reports 4 (1), 6874, 2014 | 95 | 2014 |
| Inferring latent task structure for multitask learning by multiple kernel learning C Widmer, NC Toussaint, Y Altun, G Rätsch BMC bioinformatics 11 (Suppl 8), S5, 2010 | 77 | 2010 |
| Leveraging sequence classification by taxonomy-based multitask learning C Widmer, J Leiva, Y Altun, G Rätsch Annual International Conference on Research in Computational Molecular …, 2010 | 72 | 2010 |
| Multitask Learning in Computational Biology C Widmer, G Rätsch JMLR W&CP. ICML 2011 Unsupervised and Transfer Learning Workshop. 27, 207-216, 2012 | 69 | 2012 |
| Greater power and computational efficiency for kernel-based association testing of sets of genetic variants C Lippert, J Xiang, D Horta, C Widmer, C Kadie, D Heckerman, ... Bioinformatics 30 (22), 3206-3214, 2014 | 63 | 2014 |
| Hierarchical Multitask Structured Output Learning for Large-Scale Sequence Segmentation N Görnitz, C Widmer, G Zeller, A Kahles, S Sonnenburg, G Rätsch | 43 | 2011 |
| Exploiting physico-chemical properties in string kernels NC Toussaint, C Widmer, O Kohlbacher, G Rätsch BMC bioinformatics 11 (Suppl 8), S7, 2010 | 39 | 2010 |
| Efficient Training of Graph-Regularized Multitask SVMs C Widmer, M Kloft, N Görnitz, G Rätsch ECML 2012, 2012 | 23 | 2012 |
| Tapkee: An efficient dimension reduction library S Lisitsyn, C Widmer, FJI Garcia The Journal of Machine Learning Research 14 (1), 2355-2359, 2013 | 20 | 2013 |
| Novel machine learning methods for MHC class I binding prediction C Widmer, NC Toussaint, Y Altun, O Kohlbacher, G Rätsch IAPR International Conference on Pattern Recognition in Bioinformatics, 98-109, 2010 | 16 | 2010 |
| Regularization-based multitask learning with applications to genome biology and biological imaging C Widmer, M Kloft, X Lou, G Rätsch KI-Künstliche Intelligenz 28 (1), 29-33, 2014 | 10 | 2014 |
| Multi-task learning for computational biology: Overview and outlook C Widmer, M Kloft, G Rätsch Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, 117-127, 2013 | 6 | 2013 |
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
