2014.12.19 Characterizing complex diseases by big biological data in the forms of dynamics and network

2019-07-07 00:19:02 0

定量生物学中心

学术报告

 

题目: Characterizing complex diseases by big biological data in the forms of dynamics and network

报告人: 陈洛南教授

           Key Laboratory of Systems Biology, Chinese Academy of Sciences

时间:2014-12-19(周五),13:00-14:00

地点:北京大学老化学楼东配楼102会议室

主持人:定量生物学中心,汤超教授

Abstract

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.