DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Hye Young | ko |
dc.contributor.author | Kim, Hyosil | ko |
dc.contributor.author | Na, Dokyun | ko |
dc.contributor.author | Kim, So Youn | ko |
dc.contributor.author | Jo, Deokyeon | ko |
dc.contributor.author | Lee, Doheon | ko |
dc.date.accessioned | 2016-06-29T02:06:16Z | - |
dc.date.available | 2016-06-29T02:06:16Z | - |
dc.date.created | 2016-02-12 | - |
dc.date.created | 2016-02-12 | - |
dc.date.issued | 2016-03 | - |
dc.identifier.citation | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, v.471, no.2, pp.274 - 281 | - |
dc.identifier.issn | 0006-291X | - |
dc.identifier.uri | http://hdl.handle.net/10203/208491 | - |
dc.description.abstract | Biomarkers that are identified from a single study often appear to be biologically irrelevant or false positives. Meta-analysis techniques allow integrating data from multiple studies that are related but independent in order to identify biomarkers across multiple conditions. However, existing biomarker meta-analysis methods tend to be sensitive to the dataset being analyzed. Here, we propose a meta analysis method, iMeta, which integrates t-statistic and fold change ratio for improved robustness. For evaluation of predictive performance of the biomarkers identified by iMeta, we compare our method with other meta-analysis methods. As a result, iMeta outperforms the other methods in terms of sensitivity and specificity, and especially shows robustness to study variance increase; it consistently shows higher classification accuracy on diverse datasets, while the performance of the others is highly affected by the dataset being analyzed. Application of iMeta to 59 drug-induced liver injury studies identified three key biomarker genes: Zwint, Abcc3, and Ppp1r3b. Experimental evaluation using RT-PCR and qRT-PCR shows that their expressional changes in response to drug toxicity are concordant with the result of our method. iMeta is available at http://imeta.kaist.ac.kr/index.html. (C) 2016 The Authors. Published by Elsevier Inc. | - |
dc.language | English | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.subject | GENE-EXPRESSION | - |
dc.subject | MICROARRAY | - |
dc.subject | CONSISTENCY | - |
dc.subject | CANCER | - |
dc.title | Meta-analysis method for discovering reliable biomarkers by integrating statistical and biological approaches: An application to liver toxicity | - |
dc.type | Article | - |
dc.identifier.wosid | 000372045700002 | - |
dc.identifier.scopusid | 2-s2.0-84959422555 | - |
dc.type.rims | ART | - |
dc.citation.volume | 471 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 274 | - |
dc.citation.endingpage | 281 | - |
dc.citation.publicationname | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS | - |
dc.identifier.doi | 10.1016/j.bbrc.2016.01.082 | - |
dc.contributor.localauthor | Lee, Doheon | - |
dc.contributor.nonIdAuthor | Kim, Hyosil | - |
dc.contributor.nonIdAuthor | Na, Dokyun | - |
dc.contributor.nonIdAuthor | Kim, So Youn | - |
dc.contributor.nonIdAuthor | Jo, Deokyeon | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Meta-analysis | - |
dc.subject.keywordAuthor | Biomarker discovery | - |
dc.subject.keywordAuthor | Effect size | - |
dc.subject.keywordAuthor | Drug liver toxicity | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | MICROARRAY | - |
dc.subject.keywordPlus | CONSISTENCY | - |
dc.subject.keywordPlus | CANCER | - |
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