|Tel:||+41 44 632 23 71|
|Mailing Address:||ETH Zurich
Department of Computer Science
8092 Zurich, Switzerland
Data scientist Gunnar Rätsch develops and applies advanced data analysis and modeling techniques to data from genomics, high-throughput sequencing, clinical records and images.
He earned his Ph.D. at the German National Laboratory for Information Technology under supervision of Klaus-Robert Müller. His thesis is on iterative algorithms related to Boosting and Support Vector Machines. He was a postdoc with Bob Williamson and Bernhard Schölkopf. Gunnar Rätsch received the Max Planck Young and Independent Investigator award and was leading the group on Machine Learning in Genome Biology at the Friedrich Miescher Laboratory in Tübingen (2005-2011). In 2012 he joined Memorial Sloan-Kettering Cancer Center as Associate Faculty. In May 2016 he and his lab moved to Zürich to join the Computer Science Department of ETH Zürich.
The Rätsch laboratory advances computational methods for the analysis of big data common in the biomedical sciences. The group utilizes, develops and integrates ideas from machine learning, operations research, sequence analysis, statistical genetics, text mining and computer vision with the aim to discover relationships in complex biomedical data.
- Behr J, A Kahles, Y Zhong, VT Sreedharan, P Drewe, G Rätsch. MiTie: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. Bioinformatics, 29(20):2529-38, 2013. Pubmed, PDF.
- Drewe P, O Stegle, L Hartmann L, A Kahles, R Bohnert, A Wachter, K Borgwardt, G Rätsch. Accurate detection of differential RNA processing. Nucleic Acids Res., 41(10):5189-98, 2013. Pubmed, PDF.
- Drechsel G, A Kahles, AK Kesarwani, E Stauffer, J Behr, P Drewe, G Rätsch, A Wachter. Nonsense-mediated decay of alternative precursor mRNA splicing variants is a major determinant of the Arabidopsis steady state transcriptome. Plant Cell, 25(10):3726-42, 2013. Pubmed, PDF.
- Engström PG, T Steijger, B Sipos, GR Grant, A Kahles, RGASP Consortium, G Rätsch, N Goldman, TJ Hubbard, J Harrow, R Guigo, P Bertone. Systematic evaluation of spliced aligners for RNA-seq. Nature Methods, Epub November 4th, 2013. Pubmed, PDF.
- Widmer C, M Kloft, X Lou, G Rätsch. Regularization-based multitask learning: With applications to genome biology and biomedical imaging. German Journal on Artificial Intelligence, 2013. In press.
- Chan KR, X Lou, C Crosbie, S Gardos, D Artz, G Rätsch. An empirical analysis of topic modeling for mining cancer clinical notes. In Proceedings ICDM, Data Mining and its Applications in Healthcare, 2013. In press.
- Lou X, M Kloft, G Rätsch, FA Hamprecht. Structured Learning from Cheap Data. Advanced Structured Prediction. The MIT Press, 2014. In press.
- Stein RR, V Bucci, NC Toussaint, CG Buffie, G Rätsch, EG Pamer, C Sander, and JB Xavier. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Computational Biology, October 2013. In press.
- Gan X, O Stegle O, J Behr, JG Steffen, P Drewe, KL Hildebrand, R Lyngsoe, SJ Schultheiss, EJ Osborne, VT Sreedharan, A Kahles, R Bohnert, G Jean, P Derwent, P Kersey, EJ Belfield, NP Harberd, E Kemen, C Toomajian, PX Kover, RM Clark, G Rätsch, R Mott. Multiple reference genomes and transcriptomes for A. thaliana. Nature, 477(7365):419-23, 2011. Pubmed, PDF.
- Spencer WC, G Zeller, JD Watson, SR Henz, KL Watkins, RD McWhirter, S Petersen, VT Sreedharan, C, Widmer, J Jo, V Reinke, L Petrella, S Strome, SE Von Stetina, M Katz, S Shaham, G Rätsch, DM Miller, 3rd. A spatial and temporal map of C. elegans gene expression. Genome Res, Jan 2011. Pubmed, PDF.
- Ben-Hur A, CS Ong, Sonnenburg S, Schölkopf B, G Rätsch. Support Vector Machines and Kernels for Computational Biology. PLoS Comput Biol 4(10): e1000173, 2008. Pubmed, PDF.
- Clark RM, G Schweikert, C Toomajian, S Ossowski, G Zeller, P Shinn, N Warthmann, TT Hu, G Fu, D Hinds, H Chen, K Frazer, D Huson, B Schölkopf, M Nordborg, G Rätsch, J Ecker, D Weigel. Common sequence polymorphisms shaping genetic diversity in A. thaliana. Science, 317(5836):338-342, 2007. Pubmed, PDF.
- Sonnenburg S, G Rätsch, S Schäfer, and B Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, pages 15311565, 2006. PDF.
- Rätsch, G, T Onoda, and K-R Müller. Soft Margins for AdaBoost. Machine Learning, 42(3):287 320, 2001. PDF.