MLPM2012

Over the last decade, enormous progress has been made on recording 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 no longer an unrealistic goal. 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 machine learning, 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.

Despite this rich potential of machine learning in personalized medicine, its impact on data-driven medicine remains low, due to a lack of experts with knowledge in both machine learning and in statistical genetics. Our ITN aims to close this gap by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 14 early stage researchers.