Personalized medicine

Our Bayesian, causal network based methodology exploits supercomputing technologies to integrate massive amounts of data and knowledge from different systems biology levels and clinical sources. This technology allows a unique pharmacogenomic analysis of the relevance of factors and their interaction for the process of a disease and its sequential treatment. Using a joint decision theoretic cost-utility analysis for efficacy and side-effects, this technology gives a new chance to save failed drugs by personalization and aids in the development of personalized niche-busters.

Causal model and interaction of genetic factors methotrexate treatment in leukemia.