A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

doi:10.1038/srep32448

	A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma
	Huang, Xin 1; Zeng, Jun 2; Zhou, Lina 2; Hu, Chunxiu 2; Yin, Peiyuan 2; Lin, Xiaohui 1
刊名	SCIENTIFIC REPORTS
	2016-08-31
卷号	6
ISSN号	2045-2322
DOI	10.1038/srep32448
文献子类	Article
英文摘要	Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.
WOS关键词	SUPPORT VECTOR MACHINES ; FEATURE-SELECTION ; GENE-EXPRESSION ; METABOLOMICS DATA ; SVM-RFE ; CANCER ; PREDICTION ; SIGNATURES ; DISCOVERY ; MARKERS
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	NATURE PUBLISHING GROUP
WOS记录号	WOS:000382244900001
内容类型	期刊论文
源URL	[http://cas-ir.dicp.ac.cn/handle/321008/170288]
专题	大连化学物理研究所_中国科学院大连化学物理研究所
通讯作者	Yin, Peiyuan; Lin, Xiaohui
作者单位	1.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China 2.Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
推荐引用方式 GB/T 7714	Huang, Xin,Zeng, Jun,Zhou, Lina,et al. A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma[J]. SCIENTIFIC REPORTS,2016,6.
APA	Huang, Xin,Zeng, Jun,Zhou, Lina,Hu, Chunxiu,Yin, Peiyuan,&Lin, Xiaohui.(2016).A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.SCIENTIFIC REPORTS,6.
MLA	Huang, Xin,et al."A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma".SCIENTIFIC REPORTS 6(2016).