Data Mining for Personalized Medicine

*Abstract: * Over the last decade, enormous technological advances have allowed us to record the health state of an individual patient down to the molecular level of gene activity and genomic information -- even sequencing a patient's genome for less than 1000 dollars is within reach. However, the ultimate hope to use all this information for personalized medicine, that is to tailor medical treatment to the needs of an individual, remains largely unfulfilled. To turn the vision of personalized medicine into reality, many methodological problems remain to be solved: there is a lack of methods that allow us to gain a causal understanding of the underlying disease mechanisms, including gene-gene and gene-environment interactions. Similarly, there is an urgent need for integration of the heterogeneous patient data currently available, for improved and robust biomarker discovery for disease diagnosis, prognosis and therapy outcome prediction. The field of data mining, which tries to detect patterns, rules and statistical dependencies in large datasets, has also witnessed dramatic progress over the last decade and has had a profound impact on the Internet. Amongst others, advanced methods for high-dimensional feature selection, causality inference, and data integration have been developed or are topics of current research. These techniques address many of the key methodological challenges that personalized medicine faces today and keep it from rising to the next level. In this talk, we will describe the challenges and opportunities for data mining in personalized medicine and we will present our recent research results in this direction.

There will be lunch provided after this Seminar.

Karsten Borgwardt is with The Machine Learning & Computational Biology Research Group which works at the interface of molecular biology and machine learning.   Department of Biosystems Science and Engineering at ETH Zürich.

His homepage is http://webdav.tuebingen.mpg.de/u/karsten/group/