Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms

摘要

The abundance of available information for each disease from multiple sources (e.g. as genetic, regulatory, metabolic, and protein-protein interaction) constitutes both an advantage and a challenge in identifying disease specific underlying mechanisms. Integration of multi-source data is a rising topic and a great challenge in precision medicine and is crucial in enhancing disease understanding, identifying meaningful clusters of molecular mechanisms and increasing precision and personalisation towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The overall aim of this work was to develop a novel network-based integration methodology with the following characteristics: (i) maximise the number of data sources, (ii) utilise holistic approaches to integrate these sources (iii) be simple, flexible and extendable, (iv) be conclusive. Here, we present the case of Alzheimer's disease as a paradigm for illustrating our novel approach.