题 目: Dynamics-based data science in biology and medicine ("AI for Science" and "Science for AI")
时 间: 3月6日（周一）13:00-14:00
主持人: 曾泽贤 研究员
In this talk, I will present a new concept "dynamics-based data science" in biology and medicine for studying dynamical processes and disease progressions, including dynamic network biomarkers (DNB) for early-warning signals of critical transitions, spatial-temporal information (STI) transformation for short-term time-series prediction, and partial cross-mapping (PCM) for causal inference among variables. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamics-based data-driven approaches as explicable, quantifiable, and generalizable. In particular, dynamics-based data science approaches exploit the essential features of dynamical systems in terms of data, e.g. strong fluctuations near a bifurcation point, low-dimensionality of a center manifold or an attractor, and phase-space reconstruction from a single variable by delay embedding theorem, and thus are able to provide different or additional information to the traditional approaches, i.e. statistics-based data science approaches. The dynamical-based data science approaches will further play an important role in the systematical research of various fields in biology and medicine. I will also talk recent works of "AI for Science" and "Science for AI".
陈洛南，2009年10月至今任中科院生化细胞研究所研究员，中国科学院系统生物学重点实验室执行主任，国科大杭高院首席教授。中国运筹学会《计算系统生物学分会》名誉理事长，IEEE SMC学会《系统生物学技术委员会》主席，中国生化细胞学会《分子系统生物学专业分会》主任委员。主要从事计算系统生物学、大数据分析和人工智能的研究工作。近年来发表300余篇期刊论文(包括Nature, Nature Genetics, Nature Communications, Nature Cancer, PNAS, PRL, National Science Review, Cancer Cell, Cell Research等)和四部专著(H-index: 75)。