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学科主题: 物理化学
题名: Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis
作者: Chen, Jing1;  Zhang, Yang2;  Zhang, Xiaoyan3;  Cao, Rui4, 7;  Chen, Shili1;  Huang, Qiang1;  Lu, Xin1;  Wan, Xiaoping5;  Wu, Xiaohua6;  Xu, Congjian3;  Xu, Guowang1;  Lin, Xiaohui2
通讯作者: 许国旺
关键词: Metabonomics ;  Ovarian cancer ;  Prognosis biomarker ;  Solution capacity limited EDA ;  Estimation of distribution algorithms
刊名: METABOLOMICS
发表日期: 2011-12-01
DOI: 10.1007/s11306-011-0286-3
卷: 7, 期:4, 页:614-622
收录类别: SCI
文章类型: Article
部门归属: 18
项目归属: 1808
产权排名: 1,1
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine
类目[WOS]: Endocrinology & Metabolism
摘要: Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis
研究领域[WOS]: Endocrinology & Metabolism
英文摘要: Solution capacity limited estimation of distribution algorithm (L-EDA) is proposed and applied to ovarian cancer prognosis biomarker discovery to expatiate on its potential in metabonomics studies. Sera from healthy women, epithelial ovarian cancer (EOC), recurrent EOC and non-recurrent EOC patients were analyzed by liquid chromatography-mass spectrometry. The metabolite data were processed by L-EDA to discover potential EOC prognosis biomarkers. After L-EDA filtration, 78 out of 714 variables were selected, and the relationships among four groups were visualized by principle component analysis, it was observed that with the L-EDA filtered variables, non-recurrent EOC and recurrent EOC groups could be separated, which was not possible with the initial data. Five metabolites (six variables) with P < 0.05 in Wilcoxon test were discovered as potential EOC prognosis biomarkers, and their classification accuracy rates were 86.9% for recurrent EOC and non-recurrent EOC, and 88.7% for healthy + non-recurrent EOC and EOC + recurrent EOC. The results show that L-EDA is a powerful tool for potential biomarker discovery in metabonomics study.
关键词[WOS]: RESEARCH-AND-DEVELOPMENT ;  SUPPORT VECTOR MACHINES ;  DISTRIBUTION ALGORITHMS ;  SPECTROMETRY DATA ;  FEATURE-SELECTION ;  NEURAL-NETWORKS ;  NMR-SPECTRA ;  CLASSIFICATION ;  METABOLOMICS ;  MODELS
语种: 英语
WOS记录号: WOS:000295991900015
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/115667
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.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
3.Fudan Univ, Inst Biomed Sci, Shanghai Key Lab Female Reprod Endocrine Related, Obstet & Gynecol Hosp,Shanghai Med Sch, Shanghai 200011, Peoples R China
4.Dalian Med Univ, Dept Obstet, Dalian 116033, Peoples R China
5.Shanghai Jiao Tong Univ, Sch Med, Int Peace Matern & Child Hlth Hosp, Shanghai 200030, Peoples R China
6.Fudan Univ, Dept Gynecol Oncol, Canc Hosp, Shanghai 200032, Peoples R China
7.Dalian Med Univ, Gynecol Hosp, Dalian 116033, Peoples R China

Recommended Citation:
Chen, Jing,Zhang, Yang,Zhang, Xiaoyan,et al. Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis[J]. METABOLOMICS,2011,7(4):614-622.
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