Ricerc@Sapienza

Precision medicine has been defined as an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. Nowadays, in the era of big data, the large availability of diverse health-related datasets represents a great and, at the same time, unexploited wealth for the development of new frameworks for personalized treatments. The processing and integration of multi-omics and multi-modal data represent the roads to hit for the progress of precision medicine. In this project, we propose the development of new instruments to investigate and integrate data of different nature from a network science perspective focusing on brain data. The main aim of the proposal is to develop network-based methods to analyze and integrate omics data and neuroimaging techniques. In particular, the project will focus on three lines of research: (i) disease gene prediction, (ii) analysis of gene expression data and co-expression networks, (iii) development of multi-layer network model that takes into account molecular and large-scale brain networks. The methodological developments here proposed could help in understanding our brain functioning considering at the same time molecular and system levels. Furthermore, the multi-layer and multi-modal network model could have an impact on the identification of the hallmark characteristics of neurological diseases and, consequently, on the identification of personalized treatments. The proposed framework is general in its nature, so that it could be applied exploiting also other omics data and with application in other branches of medicine.