Significant Pattern Mining

Pattern Mining is steadily gaining importance in the life sciences: Fields like Systems Biology, Genetics, or Personalized Medicine try to find patterns, that is combinations of (binary) features, that are associated with the class membership of an individual, e.g. whether the person will respond to a particular medical treatment or not.
Finding such combinations is both a computational and a statistical challenge. The computational challenge arises from the fact that a large space of candidate combinations has to be explored. The statistical challenge is due to each of these candidates representing
one hypothesis that is to be tested, resulting in an enormous multiple testing problem. While there has been substantial effort in making the search more efficient, the multiple testing problem was deemed intractable for many years. Only recently, new results started to emerge in data mining, which promise to lead to solutions for this multiple testing problem and to important applications in the biomedical domain. In our talk, we will present these recent results, including our own work in this direction.

Bio

Prof. Dr. Karsten Borgwardt is Professor of Data Mining at ETH Zürich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries. The business magazine "Capital" lists him as one of the "Top 40 under 40" in Science in/from Germany.