期刊
SCIENTIFIC REPORTS
卷 2, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/srep00813
关键词
-
资金
- NSFC [91029301, 61134013, 61072149]
- Chief Scientist Program of SIBS of CAS [2009CSP002]
- Knowledge Innovation Program of SIBS of CAS [2011KIP203]
- Shanghai Pujiang Program, 973 Program [2011CB910201]
- National Center for Mathematics and Interdisciplinary Sciences of CAS
- Aihara Project
- FIRST program from JSPS
Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据