2014.12.19 Characterizing complex diseases by big biological data in the forms of dynamics and network
Title: Complex diseases by big biological data in the forms of dynamics and network
Speaker： Prof. Luonan Chen
Key Laboratory of Systems Biology, Chinese Academy of Sciences
Time： 1:00pm Dec 19th 2014
Address： Rm 102, East wing of Old Chemistry Building, Peking Unversity
Chair： Prof. Chao Tang, Center for Quantitative Biology
We described a few new network-based methodologies for solving bio-medical problems in a dynamic manner based on big biological data. (1) we developed a new concept, edge-biomarkers, which transforms‘node expression’ data into the ‘edge expression’ data and thus can classify the phenotype of each single sample in the form of network; (2) we proposed a path-consistent analysis method based on the measurements to reconstruct gene regulatory networks, which theoretically can infer the network structure without the approximation even with a small number of samples, which cannot be achieved by the traditional approaches; (3) we derive theoretical results based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition of a disease occurs; (4) When a system is constantly perturbed by big noise, it becomes a difficult task to identify the early-warning signals of a critical transition due to the strong fluctuations of the observed data. In this work, we present a new model-free computational method based on the observed time-series data even with big noise to detect such warning signals just before the critical transition. The key idea behind this method is a new strategy: “making big noise smaller”, which increases the dimensionality of the observed data and thus makes the noise smaller in the transformed higher-dimension data.We adopted omics data of several diseases to demonstrate the effectiveness of our works.