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Support Vector Machines and Kernels for Computational Biology

Tutorial by Gunnar Rätsch at the Machine Learning Summer School 2012 in Santa Cruz, USA

SVMs are very popular in data mining and bioinformatics. This tutorial introduces SVMs and kernel algorithms and illustrates their application to typical problems in computational biology. It covers advances in kernels on strings and graphs, predicting structured outputs and discusses how to derive biological insight from the classifiers.

Note to the participants: Please bring your laptop and make sure it is connected to the internet. We will have some hands-on demonstration requiring either a web-browser or a command line (Linux or Mac).


The tutorial will be structured as follows:

  • Introduction to Machine Learning (30 minutes)
  • Support Vector Machines and Kernels (30 minutes)
  • Kernels for Sequences and Graphs (60 minutes)
  • Extracting Insight from the Learned SVM Classifier (30 minutes)
  • Structured Output Learning (30 minutes)
  • Case Studies (Applications) (30 minutes)

For the tutorial paper we have developed a Galaxy-based web service and a toolbox (easySVM) that can be easily used for most of the problems considered in the tutorial.

Further reading


Kernel Methods in Computational Biology Introduction to Computational Genomics Learning with Kernels Large-Scale Kernel Machines Semi-Supervised Learning Structured Output Learning



We gratefully acknowledge help from Sören Sonnenburg and Cheng Soon Ong for preparing an earlier version of this tutorial. Moreover, slides were contributed by Peter Gehler, Karsten Borgwardt and Petra Philips.


In case of comments, problems, questions etc. feel free to contact

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