- Home >> Latest News
Dr. Carlo Vittorio Cannistraci's Lecture: Machine Learning and Complex Networks for Complex Systems big Data Analysis and Precision Medicine
 | Title: Machine Learning and Complex Networks for Complex Systems big Data Analysis and Precision Medicine Speaker: Dr. Carlo Vittorio Cannistraci, Technical University Dresden, Germany Chair: Prof. Tianzi Jiang, Brainnetome Center, CASIA Time: 09:30-10:30, Aug. 24, 2018 Venue: 714 meeting room, 7th floor, Intelligence Building |
I will present our research at the Biomedical Cybernetics Group that I established about four years ago in Technical University Dresden. We adopt a transdisciplinary approach integrating information theory, machine learning and network science to investigate the physics of adaptive complex networked systems at different scales, from molecules to ecological and social systems, with a particular attention to biology and medicine, and a new emerging interest for the analysis of complex big data in social and economic science. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (such as topology, network and manifold theory) to characterize many-body interactions in complex systems. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network analysis. In particular, we deal with: prediction of wiring in networks and multiscale-combinatorial marker design for quantification of topological modifications in complex networks. Our attention for precision biomedicine is aimed to topics with important impact from the economical point of view such as development of tools for disease biomarker discovery, drug repositioning and combinatorial drug therapy.
This talk will focus on two main theoretical innovation. Firstly, the development of machine learning for topological estimation of nonlinear relations in high-dimensional data (or in complex networks) and its relevance for applications in big data, with a particular emphasis on brain connectome analysis. Secondly, we will discuss the Local Community Paradigm (LCP), which is a brain-inspired theory proposed to model local-topology-dependent link-growth in complex networks and therefore it is useful to devise topological methods for link prediction in monopartite and bipartite networks such as molecular drug-target interactions and product-consumer networks.
Biography
Carlo Vittorio Cannistraci is a theoretical engineer, head of the Biomedical Cybernetics Group and faculty of the Department of Physics in the Technical University Dresden, which is a member of the TU9 excellence-league (the nine most prestigious technical universities in Germany). Carlo's area of research embraces information theory, machine learning and complex networks including also applications in systems biomedicine and neuroscience. Nature Biotechnology selected Carlo's article (Cell 2010) on machine learning in developmental biology to be nominated in the list of 2010 notable breakthroughs in computational biology. Circulation Research featured Carlo's work (Circulation Research 2012) on leveraging a cardiovascular systems biology strategy to predict future outcomes in heart attacks, commenting: "a space-aged evaluation using computational biology". The Technical University Dresden honoured Carlo of the Young Investigator Award 2016 in Physics for his work on the local-community-paradigm theory and link prediction in monopartite and bipartite networks. In 2017, Springer-Nature scientific blog highlighted with an interview to Carlo his study on "How the brain handles pain through the lens of network science". The American Heart Association covered this year on its website the recent chronobiology discovery of Carlo on how the sunshine affects the risk and time onset of heart attack. In 2018, Nature Communications featured Carlo’s article entitled "Machine learning meets complex networks via coalescent embedding in the hyperbolic space" in the selected interdisciplinary collection of recent research on complex systems.