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A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks
Huang, Xin1; Lin, Xiaohui1; Zeng, Jun2; Wang, Lichao2; Yin, Peiyuan2; Zhou, Lina2; Hu, Chunxiu2; Yao, Weihong1
Source PublicationSCIENTIFIC REPORTS
2017-10-30
DOI10.1038/s41598-017-14682-5
Volume7
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology
WOS SubjectMultidisciplinary Sciences
WOS Research AreaScience & Technology - Other Topics
WOS KeywordCARDIOVASCULAR-DISEASE ; METABOLOMICS DATA ; GENE-EXPRESSION ; CANCER ; CLASSIFICATION ; IDENTIFICATION ; CARCINOMA ; SELECTION ; MODULES ; MARKERS
AbstractAnalyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PBDSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals.
Language英语
WOS IDWOS:000414131700032
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/149782
Collection中国科学院大连化学物理研究所
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,Lin, Xiaohui,Zeng, Jun,et al. A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks[J]. SCIENTIFIC REPORTS,2017,7.
APA Huang, Xin.,Lin, Xiaohui.,Zeng, Jun.,Wang, Lichao.,Yin, Peiyuan.,...&Yao, Weihong.(2017).A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.SCIENTIFIC REPORTS,7.
MLA Huang, Xin,et al."A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks".SCIENTIFIC REPORTS 7(2017).
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