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A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma
Huang, Xin1; Zeng, Jun2; Zhou, Lina2; Hu, Chunxiu2; Yin, Peiyuan2; Lin, Xiaohui1
Source PublicationSCIENTIFIC REPORTS
2016-08-31
ISSN2045-2322
DOI10.1038/srep32448
Volume6
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology
WOS SubjectMultidisciplinary Sciences
WOS Research AreaScience & Technology - Other Topics
WOS KeywordSUPPORT VECTOR MACHINES ; FEATURE-SELECTION ; GENE-EXPRESSION ; METABOLOMICS DATA ; SVM-RFE ; CANCER ; PREDICTION ; SIGNATURES ; DISCOVERY ; MARKERS
AbstractTime-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.
Language英语
WOS IDWOS:000382244900001
PublisherNATURE PUBLISHING GROUP
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/170289
Collection中国科学院大连化学物理研究所
Corresponding AuthorYin, Peiyuan; Lin, Xiaohui
Affiliation1.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
Recommended Citation
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).
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