Present-day era of Big Data provides the unique opportunity to develop innovative approaches for data analysis to find new insights into specialized fields of biomedical research such as Precision Medicine [1]. Precision Medicine is defined as the integration of molecular research with clinical data in order to deliver better diagnoses and treatments tailored to the individual characteristics of each patient. Advanced analysis of health related data that is specific to a given individual must focus on both clinical information (e.g. clinical reports, medical images, patient histories) and biological data (e.g. gene and protein sequences, functions and pathways). This wealth of information has the potential to inspire systematic ways of making sense from the massive and heterogeneous stream of data and providing a unified view. In the regards, Deep Learning (DL) [2] and Cognitive Computing (CC) [3] are two branches of Artificial Intelligence (AI) representing convenient choices to tackle the problem of Big Data integration for Precision Medicine. DL comprises several machine learning techniques modeling multiple representations of data through many layers of nonlinear processing units. CC is a cross-disciplinary technology for adaptive and contextual knowledge representation and reasoning through sophisticated analytics aiming to mimic human learning mechanisms
