Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.
Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as we...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/14/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121269735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121269735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/14/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 121269735 121269735 121269735 10.1155/2017/2437608 121269735 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Metabolomic Biomarker Identification in Presence of Outliers and Missing Values. aug: au: Kumar, Nishith Hoque, Md. Aminul Shahjaman, Md. Islam, S. M. Shahinul Mollah, Md. Nurul Haque affil: Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh sug: subj: Metabolism Data Analysis Carcinoma, Hepatocellular Familial and Genetic Tumor Markers, Biological Algorithms Genotype Phenotype Descriptive Statistics T-Tests Data Analysis Software Regression Post Hoc Analysis P-Value Simulations One-Way Analysis of Variance False Positive Results ROC Curve Validity False Negative Results Sensitivity and Specificity Gas Chromatography-Mass Spectrometry ab: Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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