• Digital-Health-Conference-2020

Invited SpeakersProfile Details

Prof. Luonan Chen
Prof. Luonan Chen Luonan Chen is a Professor of Biological Sciences at Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China.

Biography

Luonan Chen received a BS degree in Electrical Engineering, from Huazhong University of Science and Technology, China, and the M.E. and Ph.D. degrees in electrical engineering, from Tohoku University, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. He was elected as the founding president of the Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at the IEEE SMC Society. His research areas include nonlinear dynamics, causality inference, machine learning, and systems biology, in particular, dynamics-based data analysis. In recent years, he published over 350 journal papers and two monographs in the area of network and systems biology.


All sessions by Prof. Luonan Chen

  • Day 3Wednesday, January 22nd
Session 5 : AI & Computational Resources (Chair Dr. Katsuhiko Mineta)
9:30 am

KEYNOTE LECTURE: Predictive and preventive medicine by dynamic network biomarker

Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions (un-occurred diseases), even with only a small number of samples.

Specifically, we theoretically derive an index based on a dynamic network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition occurs [1][2]. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data.

We employ gene expression data of three diseases to demonstrate the effectiveness of our method for predictive and preventive medicine. The relevance of DNBs with the diseases was also validated by related experimental data (e.g., liver cancer, lung injury, influenza, type-2 diabetes) and functional analysis. DNB can also be used for the analysis of nonlinear biological processes.

Building 19, Hall 1 09:30 - 10:05 Details