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题名: A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks
作者: Huang, Xin1;  Lin, Xiaohui1;  Zeng, Jun2;  Wang, Lichao2;  Yin, Peiyuan2;  Zhou, Lina2;  Hu, Chunxiu2;  Yao, Weihong1
刊名: SCIENTIFIC REPORTS
发表日期: 2017-10-30
DOI: 10.1038/s41598-017-14682-5
卷: 7
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Multidisciplinary Sciences
研究领域[WOS]: Science & Technology - Other Topics
英文摘要: Analyzing 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.
关键词[WOS]: CARDIOVASCULAR-DISEASE ;  METABOLOMICS DATA ;  GENE-EXPRESSION ;  CANCER ;  CLASSIFICATION ;  IDENTIFICATION ;  CARCINOMA ;  SELECTION ;  MODULES ;  MARKERS
语种: 英语
WOS记录号: WOS:000414131700032
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/149719
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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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

Recommended Citation:
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.
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