DISSECTING COMPLEXITY OF CHRONIC DISEASES WITH ARTIFICIAL ADAPTIVE SYSTEMS
Como, 26-30 May 2014
About the course
Complex systems is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. Many chronic diseases behave like complex systems. For the most part, complex diseases are caused by a combination of genetic, environmental, and lifestyle factors, most of which have not yet been identified. The vast majority of diseases fall into this category, including several congenital defects and a number of adult-onset diseases. Some examples include Alzheimer’s disease, obesity, multiple sclerosis, osteoporosis, connective tissue diseases, kidney diseases, autoimmune diseases, and many more.
Complex diseases pose a challenge for mathematics and mathematical sciences with their formal modeling and simulation.
The equations from which complex system models are developed generally derive from statistical physics, information theory and non-linear dynamics, and run in powerful computers.
The course will focus the application of advanced statistical algorithms, with particular regard to artificial adaptive systems and other machine learning algorithms to real world data sets in paradigmatic fields of medicine like Alzheimer disease and other chronic degenerative disorders aiming to model key targets in individual patients or to perform intelligent data mining for new hypothesis generation on the underlying pathogenetic mechanisms.
Financial support for young researchers available! To apply for support, see registration form.
Organized in the frame of Lake Como School of Advanced Studies with support of Fondazione Cariplo