Abstract:
: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.
journal_name
Biomed Res Intjournal_title
BioMed research internationalauthors
Kumar N,Hoque MA,Shahjaman M,Islam SM,Mollah MNdoi
10.1155/2017/2437608subject
Has Abstractpub_date
2017-01-01 00:00:00pages
2437608eissn
2314-6133issn
2314-6141journal_volume
2017pub_type
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journal_title:BioMed research international
pub_type: 杂志文章,评审
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pub_type: 临床试验,杂志文章
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