Data-driven Computational Systems Biology for Complex Diseases
*洛南 陈 (中国科学院上海生命科学研究院) email@example.com
I will talk about the recent works of my group on the area of computational systems biology, in particular, for nonlinear dynamical analysis and the statistical analysis on complex diseases. It includes, (1) edge biomarker, network biomarker, and further dynamic network biomarker for detecting the tipping points of complex diseases; (2) network inference by sample-based data, and network reconstruction by time-series data; (3) causality inference based on statistical dependency, and causality detection based on dynamical systems.
 Biwei Yang, Meiyi Li, Wenqing Tang, Weixin Liu, Si Zhang, Luonan Chen, Jinglin Xia. Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nature Communications, DOI: 10.1038/s41467-018-03024-2, 2018.
 Juan Zhao, Yiwei Zhou, Xiujun Zhang, Luonan Chen. Part mutual information for quantifying direct associations in networks. Proc Natl Acad Sci USA, 2016, 113, 5130-5135.
 Xiangtian Yu, Jingsong Zhang, Shaoyan Sun, Xin Zhou, Tao Zeng, Luonan Chen; Individual-specific edge-network analysis for disease prediction, Nucleic Acids Research, 45(20):e170, doi: 10.1093/nar/gkx787, 2017
 Xiaoping Liu, Xiao Chang, Rui Liu, Xiangtian Yu, Luonan Chen and Kazuyuki Aihara. Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLoS Computational Biology, 13(7):e1005633, doi: 10.1371/journal.pcbi.1005633, 2017.