DICP OpenIR
Subject Area物理化学
Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis
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; Xu GW(许国旺)
KeywordMetabonomics Ovarian Cancer Prognosis Biomarker Solution Capacity Limited Eda Estimation Of Distribution Algorithms
Source PublicationMETABOLOMICS
2011-12-01
ISSN待补充
DOI10.1007/s11306-011-0286-3
Volume7Issue:4Pages:614-622
Indexed BySCI
SubtypeArticle
Department18
Funding Project1808
Contribution Rank1,1
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS SubjectEndocrinology & Metabolism
WOS Research AreaEndocrinology & Metabolism
WOS KeywordRESEARCH-AND-DEVELOPMENT ; SUPPORT VECTOR MACHINES ; DISTRIBUTION ALGORITHMS ; SPECTROMETRY DATA ; FEATURE-SELECTION ; NEURAL-NETWORKS ; NMR-SPECTRA ; CLASSIFICATION ; METABOLOMICS ; MODELS
AbstractApplication of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis; 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.
Language英语
WOS IDWOS:000295991900015
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/115667
Collection中国科学院大连化学物理研究所
Corresponding AuthorXu GW(许国旺)
Affiliation1.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
GB/T 7714
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.
APA Chen, Jing.,Zhang, Yang.,Zhang, Xiaoyan.,Cao, Rui.,Chen, Shili.,...&许国旺.(2011).Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis.METABOLOMICS,7(4),614-622.
MLA Chen, Jing,et al."Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis".METABOLOMICS 7.4(2011):614-622.
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