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Predicting future dynamics and quantifying criticality in complex systems

编辑:wfy 时间:2019年03月08日 访问次数:855

题目:Predicting future dynamics and quantifying criticality in complex systems

报告人:Luonan Chen (中国科学院上海生命科学研究院)

时间:2019.04.02(周二)下午 3:00

地点:紫金港校区管理学院行政楼14楼1417报告厅


摘要:I will talk two data-driven methods for the analysis of complex systems based on high-dimensional small-sample data: (1) randomly distributed embedding (RDE) for future dynamics prediction on steady states, and (2) dynamic network marker or dynamic network biomarker (DNB) for criticality quantification near critical states (or tipping points). The main results are summarized in the following papers. 

[1] Huanfei Ma, Siyang Leng, Kazuyuki Aihara, Wei Lin, Luonan Chen. Randomly Distributed Embedding Making Short-term High-dimensional Data Predictable. Proc Natl Acad Sci USA, 115 (43) E9994-E10002, https://doi.org/10.1073/pnas.1802987115, 2018. 

[2] Xiaoping Liu, Xiao Chang, Siyang Leng, Hui Tang, Kazuyuki Aihara, Luonan Chen. Detection for disease tipping points by landscape dynamic network biomarkers. Natl Sci Rev, 2019, https://doi.org/10.1093/nsr/nwy162. 

[3] 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, 9, 678, DOI: 10.1038/s41467-018-03024-2, 2018. 


欢迎广大师生踊跃参加!

联系人:蒋杭进 (jianghj@zju.edu.cn)