标题:Identifying critical transitions of complex diseases based on a single sample
作者:Liu, Rui; Yu, Xiangtian; Liu, Xiaoping; Xu, Dong; Aihara, Kazuyuki; Chen, Luonan
作者机构:[Liu, Rui] S China Univ Technol, Sch Math, Guangzhou 510640, Guangdong, Peoples R China.; [Yu, Xiangtian; Chen, Luonan] Chinese Acad Sci, Shanghai I 更多
通讯作者:Chen, LN
通讯作者地址:[Chen, LN]Chinese Acad Sci, Shanghai Inst Biol Sci, SIBS Novo Nordisk Translat Res Ctr PreDiabet, Key Lab Syst Biol, Shanghai 200031, Peoples R China.
来源:BIOINFORMATICS
出版年:2014
卷:30
期:11
页码:1579-1586
DOI:10.1093/bioinformatics/btu084
摘要:Motivation: Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-disease state, but this requires multiple samples to reach a correct diagnosis for one individual, thereby restricting its clinical application.; Results: In this article, we have developed a novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Our approach has been validated by the successful identification of pre-disease samples from subjects or individuals before the emergence of disease symptoms for acute lung injury, influenza and breast cancer.
收录类别:SCOPUS;SCIE
WOS核心被引频次:28
Scopus被引频次:35
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901315597&doi=10.1093%2fbioinformatics%2fbtu084&partnerID=40&md5=75f2f7e9b974ddb62102cf65791d161c
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