The underlying concept of precision medicine, in which health care is individually tailored on the basis of the variability of person's genes, lifestyle and environment, is not new. This approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies will be the best for each patient and for a particular disease.
Recently, it is becoming attainable, thanks to the availability of large-scale biological databases containing information of many samples and phenotypes, of genes and drugs, and of detailed epidemiological data. To analyze and integrate such large information into predictive models, there is a need for new computational approaches, intended to build testable models that will lead to evidence-based predictors that may guide future clinical practice.
Our computational framework is capable to improve new knowledge in various areas of biology and medicine. In particular, in the field of molecular networks, cellular physiology and tissues, and systems biology. It allows the identification of optimal therapeutic targets associated with genomic and proteomic biomarkers; the help in the design of new drugs that minimize the side effects and maximize the therapeutic response; the identification of diagnostic and prognostic biomarkers; the design and dynamic improvement of personalized therapies.