Cited by 161Gene expression and cancer classificationBioinformatics and Genomic NetworksBreast Cancer Treatment Studies

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Meta-analytic support vector machine for integrating multiple omics data

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Kim, SungHwan, Jhong, Jae-Hwan, Lee, JungJun, Koo, Ja-Yong·2017·BioData Mining

DOI 10.1186/s13040-017-0126-8PMC PMC5270233

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Abstract

BACKGROUND: Of 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. RESULTS: Experimental studies show that the Meta-SVM is superior to the existing meta-analysis method in detecting true signal genes. In real data applications, diverse omics data of breast cancer (TCGA) and mRNA expression data of lung disease (idiopathic pulmonary fibrosis; IPF) were applied. As a result, we identified gene sets consistently associated with the diseases across studies. In particular, the ascertained gene set of TCGA omics data was found to be significantly enriched in the ABC transporters pathways well known as critical for the breast cancer mechanism. CONCLUSION: The Meta-SVM effectively achieves the purpose of meta-analysis as jointly leveraging multiple omics data, and facilitates identifying potential biomarkers and elucidating the disease process.

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Authors: Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y.
Affiliations: Korea University (KR)
Corresponding author: Koo, Ja-Yong
ORCID: Kim, SungHwan
Jhong, Jae-Hwan
Lee, JungJun
Koo, Ja-Yong
Venue: BioData Mining
Publisher: Springer Science and Business Media LLC
Published: 2017-01-26
Volume: 10
Issue: 1
Pages: 2-2
ISSN: 1756-0381 (ISSN-L)
DOI: 10.1186/s13040-017-0126-8
PMC: PMC5270233
OpenAlex: W2583025663
Cited by: 159
References: 53
Citations by year: 2026: 13 · 2025: 18 · 2024: 23 · 2023: 20 · 2022: 22 · 2021: 22 · 2020: 19 · 2019: 9 · 2018: 10 · 2017: 3
FWCI: 4.58
Type: journal article
Language: en
Update notices: correction (2017-02-14)
Open access: gold
doi.org
Licence: cc-by
Locations: BioData Mining (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: The National Research Foundation of Korea (NRF) (NRF-2013R1A1A2008619)
The National Research Foundation Korea (NRF) (2016R1A6A3A0100942)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords: multi-omics integration, gene selection, biomarker discovery, breast cancer, idiopathic pulmonary fibrosis, ABC transporters, pathway enrichment

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Meta-analytic support vector machine for integrating multiple omics data

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Integrity signals

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