Widmer, C., Rätsch, G.
Multitask Learning in Computational Biology
JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop, 27, pages: 207-216, 2012 (article)
Widmer, C., Kloft, M., Görnitz, N., Rätsch, G.
Efficient Training of Graph-Regularized Multitask SVMs
In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD 2012, LNCS Vol. 7523, pages: 633-647, (Editors: PA Flach and T De Bie and N Cristianini), Springer, Berlin, Germany, ECML, 2012 (inproceedings)
Gan, X., Stegle, O., Behr, J., Steffen, J., Drewe, P., Hildebrand, K., Lyngsoe, R., Schultheiss, S., Osborne, E., Sreedharan, V., Kahles, A., Bohnert, R., Jean, G., Derwent, P., Kersey, P., Belfield, E., Harberd, N., Kemen, E., Toomajian, C., Kover, P., Clark, R., Rätsch, G., Mott, R.
Multiple reference genomes and transcriptomes for Arabidopsis thaliana
Nature, 477(7365):419–423, September 2011 (article)
Görnitz, N., Widmer, C., Zeller, G., Kahles, A., Sonnenburg, S., Rätsch, G.
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation
In Advances in Neural Information Processing Systems 24, pages: 2690-2698, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and FCN Pereira and KQ Weinberger), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)
Stegle, O., Drewe, P., Bohnert, R., Borgwardt, K., Rätsch, G.
Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts
Nature Precedings, 2010, pages: 1-11, May 2010 (article)
Widmer, C., Leiva, J., Altun, Y., Rätsch, G.
Leveraging Sequence Classification by Taxonomy-based Multitask Learning
In Research in Computational Molecular Biology, LNCS, Vol. 6044, pages: 522-534, (Editors: B Berger), Springer, Berlin, Germany, 14th Annual International Conference, RECOMB, 2010 (inproceedings)
Widmer, C., Toussaint, N., Altun, Y., Kohlbacher, O., Rätsch, G.
Novel machine learning methods for MHC Class I binding prediction
In Pattern Recognition in Bioinformatics, pages: 98-109, (Editors: TMH Dijkstra and E Tsivtsivadze and E Marchiori and T Heskes), Springer, Berlin, Germany, 5th IAPR International Conference, PRIB, 2010 (inproceedings)
Widmer, C., Toussaint, N., Altun, Y., Rätsch, G.
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
BMC Bioinformatics, 11 Suppl 8, pages: S5, 2010 (article)
Schweikert, G., Widmer, C., Schölkopf, B., Rätsch, G.
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
In Advances in neural information processing systems 21, pages: 1433-1440, (Editors: D Koller and D Schuurmans and Y Bengio and L Bottou), Curran, Red Hook, NY, USA, 22nd Annual Conference on Neural Information Processing Systems (NIPS), June 2009 (inproceedings)
Graf, A., Bousquet, O., Rätsch, G., Schölkopf, B.
Prototype Classification: Insights from Machine Learning
Neural Computation, 21(1):272-300, January 2009 (article)
Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C., Philips, P., De Bona, F., Hartmann, L., Bohlen, A., Krüger, N., Sonnenburg, S., Rätsch, G.
mGene: accurate SVM-based gene finding with an application to nematode genomes
Genome Research, 19(11):2133-43, 2009 (article)
Schweikert, G., Behr, J., Zien, A., Zeller, G., Ong, C., Sonnenburg, S., Rätsch, G.
mGene.web: a web service for accurate computational gene finding
Nucleic Acids Research, 37, pages: W312-6, 2009 (article)
Ben-Hur, A., Ong, C., Sonnenburg, S., Schölkopf, B., Rätsch, G.
Support Vector Machines and Kernels for Computational Biology
PLoS Computational Biology, 4(10: e1000173):1-10, October 2008 (article)
Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G.
mGene: A Novel Discriminative Gene Finder
Worm Genomics and Systems Biology meeting, July 2008 (talk)
Rätsch, G., Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthman, N., Hu, T., Fu, G., Hinds, D., Cheng, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D., Schneeberger, K., Bohlen, A.
Discovering Common Sequence Variation in
Arabidopsis thaliana
16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)
Laubinger, S., Zeller, G., Henz, S., Sachsenberg, T., Widmer, C., Naouar, N., Vuylsteke, M., Schölkopf, B., Rätsch, G., Weigel, D.
At-TAX: A Whole Genome Tiling Array Resource for Developmental Expression Analysis and Transcript Identification in Arabidopsis thaliana
Genome Biology, 9(7: R112):1-16, July 2008 (article)
Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.
Positional Oligomer Importance Matrices
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)
Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.
Machine Learning Algorithms for Polymorphism Detection
NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)
Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.
Accurate Splice site Prediction Using Support Vector Machines
BMC Bioinformatics, 8(Supplement 10):1-16, December 2007 (article)
Sonnenburg, S., Braun, M., Ong, C., Bengio, S., Bottou, L., Holmes, G., LeCun, Y., Müller, K., Pereira, F., Rasmussen, C., Rätsch, G., Schölkopf, B., Smola, A., Vincent, P., Weston, J., Williamson, R.
The Need for Open Source Software in Machine Learning
Journal of Machine Learning Research, 8, pages: 2443-2466, October 2007 (article)
Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthmann, N., Hu, T., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Rätsch, G., Ecker, J., Weigel, D.
Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana
Science, 317(5836):338-342, July 2007 (article)
Schulze, U., Hepp, B., Ong, C., Rätsch, G.
PALMA: mRNA to Genome Alignments using Large Margin Algorithms
Bioinformatics, 23(15):1892-1900, May 2007 (article)
Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., Schölkopf, B.
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning
PLoS Computational Biology, 3(2, e20):0313-0322, February 2007 (article)
Schweikert, G., Zeller, G., Zien, A., Ong, C., de Bona, F., Sonnenburg, S., Phillips, P., Rätsch, G.
Ab-initio gene finding using machine learning
NIPS Workshop on New Problems and Methods in Computational Biology, December 2006 (talk)
Rätsch, G., Hepp, B., Schulze, U., Ong, C.
PALMA: Perfect Alignments using Large Margin Algorithms
In GCB 2006, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)
Shin, H., Hill, N., Rätsch, G.
Graph Based Semi-Supervised Learning with Sharper Edges
In ECML 2006, pages: 401-412, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)
Schweikert, G., Zeller, G., Clark, R., Ossowski, S., Warthmann, N., Shinn, P., Frazer, K., Ecker, J., Huson, D., Weigel, D., Schölkopf, B., Rätsch, G.
Machine Learning Algorithms for Polymorphism Detection
2nd ISCB Student Council Symposium, August 2006 (talk)
Sonnenburg, S., Zien, A., Rätsch, G.
ARTS: Accurate Recognition of Transcription Starts in Human
Bioinformatics, 22(14):e472-e480, July 2006 (article)
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.
Large Scale Multiple Kernel Learning
Journal of Machine Learning Research, 7, pages: 1531-1565, July 2006 (article)
Clark, R., Ossowski, S., Schweikert, G., Rätsch, G., Shinn, P., Zeller, G., Warthmann, N., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D.
An Inventory of Sequence Polymorphisms For Arabidopsis
17th International Conference on Arabidopsis Research, April 2006 (talk)
Tsuda, K., Rätsch, G., Warmuth, M.
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection
In Advances in Neural Information Processing Systems 17, pages: 1425-1432, (Editors: Saul, L.K. , Y. Weiss, L. Bottou), MIT Press, Cambridge, MA, USA, Eighteenth Annual Conference on Neural Information Processing Systems (NIPS), July 2005 (inproceedings)
Tsuda, K., Rätsch, G.
Image Reconstruction by Linear Programming
IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)
Rätsch, G., Sonnenburg, S., Schölkopf, B.
RASE: recognition of alternatively spliced exons in C.elegans
Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)
Tsuda, K., Rätsch, G., Warmuth, M.
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)
Sonnenburg, S., Rätsch, G., Schölkopf, B.
Large Scale Genomic Sequence SVM Classifiers
In Proceedings of the 22nd International Conference on Machine Learning, pages: 849-856, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)
Bousquet, O., von Luxburg, U., Rätsch, G.
Advanced Lectures on Machine Learning
ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)
Tsuda, K., Rätsch, G.
Image Construction by Linear Programming
In Advances in Neural Information Processing Systems 16, pages: 57-64, (Editors: Thrun, S., L.K. Saul, B. Schölkopf), MIT Press, Cambridge, MA, USA, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)
Tsuda, K., Rätsch, G.
Image Reconstruction by Linear Programming
(118), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, October 2003 (techreport)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.
Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)
Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.
A New Discriminative Kernel from Probabilistic Models
Neural Computation, 14(10):2397-2414, October 2002 (article)
Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.
A new discriminative kernel from probabilistic models
In Advances in Neural Information Processing Systems 14, pages: 977-984, (Editors: Dietterich, T.G. , S. Becker, Z. Ghahramani), MIT Press, Cambridge, MA, USA, Fifteenth Annual Neural Information Processing Systems Conference (NIPS), September 2002 (inproceedings)
Rätsch, G., Mika, S., Schölkopf, B., Müller, K.
Constructing Boosting algorithms from SVMs: an application to one-class classification.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)
Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.
An Introduction to Kernel-Based Learning Algorithms
IEEE Transactions on Neural Networks, 12(2):181-201, March 2001 (article)
Tsuda, K., Rätsch, G., Mika, S., Müller, K.
Learning to predict the leave-one-out error of kernel based classifiers
In International Conference on Artificial Neural Networks, ICANN'01, (LNCS 2130):331-338, (Editors: G. Dorffner, H. Bischof and K. Hornik), International Conference on Artificial Neural Networks, ICANN'01, 2001 (inproceedings)
Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.
Robust ensemble learning
In Advances in Large Margin Classifiers, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites
Bioinformatics, 16(9):799-807, September 2000 (article)
Rätsch, G., Schölkopf, B., Smola, A., Müller, K., Onoda, T., Mika, S.
v-Arc: Ensemble Learning in the Presence of Outliers
In Advances in Neural Information Processing Systems 12, pages: 561-567, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.
Invariant feature extraction and classification in kernel spaces
In Advances in neural information processing systems 12, pages: 526-532, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)
Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.
An Introduction to Kernel-Based Learning Algorithms
In Handbook of Neural Network Signal Processing, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)
Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.
Robust Ensemble Learning for Data Mining
In Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1805, pages: 341-341, Lecture Notes in Artificial Intelligence, (Editors: H. Terano), Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000 (inproceedings)