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.
The three lines of research described in this proposal have the goal to develop a multi-layer and multi-modal network-based model taking into account the human interactome, gene co-expression networks, and large-scale brain networks. The main challenge in integrating these diverse levels (to which we want to contribute) is to stack the networks aligning and assigning the brain anatomical locations from which the different data originate.
The development of this new model could help in understanding our brain functioning considering at the same time molecular and system levels. Furthermore, it could have an impact on the identification of the hallmark characteristics of neurological diseases and, consequently, on the identification of personalized treatments. Indeed, each level of the model can become disease specific. For example, disease genes (those already known, and the putative ones returned by disease gene prediction task) can be projected into the human interactome: here, "disease perturbations" can be studied focusing on the connectivity patterns of disease genes. Furthermore, the use of gene expression and large-scale brain data from patient cohorts (e.g., Alzheimer's patients or patients suffering from other neurodegenerative diseases) can provide insights in the micro- and macro-scale brain circuits disrupted as a result of the disease. Considering the three levels at the same time is the greatest innovation of our project. In fact, usually, molecular and large-scale neurophysiological data are treated and investigated separately. Even though in the last decades these investigations led to important discoveries, it is important to bear in mind that micro-scale and macro-scale brain interactions operate synergistically, allowing our brain to perform specific functions and shaping our cognition and behaviour.
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.