Meta-analysis method for discovering reliable biomarkers by integrating statistical and biological approaches: An application to liver toxicity
- Authors
- Cho, Hyeyoung; Kim, Hyosil; Na, Dokyun; Kim, So Youn; Jo, Deokyeon; Lee, Doheon
- Issue Date
- Mar-2016
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Keywords
- Meta-analysis; Biomarker discovery; Effect size; Drug liver toxicity
- Citation
- BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, v.471, no.2, pp 274 - 281
- Pages
- 8
- Journal Title
- BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
- Volume
- 471
- Number
- 2
- Start Page
- 274
- End Page
- 281
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/7160
- DOI
- 10.1016/j.bbrc.2016.01.082
- ISSN
- 0006-291X
1090-2104
- 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.
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Collections - College of ICT Engineering > School of Integrative Engineering > 1. Journal Articles
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