Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

doi:10.1371/journal.pcbi.1005633

	Quantifying critical states of complex diseases using single-sample dynamic network biomarkers
	Liu, Xiaoping 1,2,3,4; Chang, Xiao 1,2; Liu, Rui 5; Yu, Xiangtian 3; Chen, Luonan1,3,6 ; Aihara, Kazuyuki 1
	2017-07
发表期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
卷号	13 期号:7
发表状态	已发表
DOI	10.1371/journal.pcbi.1005633
摘要	Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to "diagnose disease", sDNB is based on the information of differential associations, thereby having the ability to "predict disease" or "diagnose near-future disease". Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	JSPS KAKENHI[15H05707]
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000406619800023
出版者	PUBLIC LIBRARY SCIENCE
WOS关键词	CELL LUNG-CANCER ; BETA-III-TUBULIN ; COLORECTAL-CANCER ; GENE-EXPRESSION ; POOR-PROGNOSIS ; FIBRONECTIN 1 ; PANCREATIC-CANCER ; GASTRIC-CANCER ; METASTASIS ; PROGRESSION
原始文献类型	Article
通讯作者	Chen, Luonan ; Aihara, Kazuyuki
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/4531
专题	生命科学与技术学院_特聘教授组_陈洛南组
通讯作者	Chen, Luonan; Aihara, Kazuyuki
作者单位	1.Univ Tokyo, Inst Ind Sci, Tokyo, Japan 2.Anhui Univ Finance & Econ, Coll Stat & Appl Math, Bengbu, Anhui, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Mol Cell Sci, Key Lab Syst Biol,Shanghai Inst Biol Sci, Inst Biochem & Cell Biol,Innovat Ctr Cell Signal, Shanghai, Peoples R China 4.Shandong Univ Weihai, Sch Math & Stat, Weihai, Peoples R China 5.South China Univ Technol, Sch Math, Guangzhou, Guangdong, Peoples R China 6.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
通讯作者单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Liu, Xiaoping,Chang, Xiao,Liu, Rui,et al. Quantifying critical states of complex diseases using single-sample dynamic network biomarkers[J]. PLOS COMPUTATIONAL BIOLOGY,2017,13(7).
APA	Liu, Xiaoping,Chang, Xiao,Liu, Rui,Yu, Xiangtian,Chen, Luonan,&Aihara, Kazuyuki.(2017).Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.PLOS COMPUTATIONAL BIOLOGY,13(7).
MLA	Liu, Xiaoping,et al."Quantifying critical states of complex diseases using single-sample dynamic network biomarkers".PLOS COMPUTATIONAL BIOLOGY 13.7(2017).