Meta-analytic support vector machine for integrating multiple omics data

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• 作者：SungHwan Kim ; Jae-Hwan Jhong ; JungJun Lee ; Ja-Yong Koo
• 关键词：Support vector machine ; Meta ; analysis ; Data integration ; TCGA
• 刊名：BioData Mining
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：10
• 期：1
• 全文大小：447KB
• 刊物主题：Computer Appl. in Life Sciences; Computational Biology/Bioinformatics; Data Mining and Knowledge Discovery; Bioinformatics; Algorithms;
• 出版者：BioMed Central
• ISSN：1756-0381
• 卷排序：10

文摘

BackgroundOf late, high-throughput microarray and sequencing data have been extensively used to monitor biomarkers and biological processes related to many diseases. Under this circumstance, the support vector machine (SVM) has been popularly used and been successful for gene selection in many applications. Despite surpassing benefits of the SVMs, single data analysis using small- and mid-size of data inevitably runs into the problem of low reproducibility and statistical power. To address this problem, we propose a meta-analytic support vector machine (Meta-SVM) that can accommodate multiple omics data, making it possible to detect consensus genes associated with diseases across studies.