The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Meta-analytic support vector machine for integrating multiple omics data
Open accessAbstract
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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“Meta-SVM (Meta-analytic SVM) is proposed by Kim (Kim et al., 2017), which integrates multiple omics data and able to detect consensus genes associated with diseases across studies such as breast cancer and idiopathic pulmonary fibrosis.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Kim et al., 2017
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Cited by
Sorted by citations
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
Huang, S., Cai, N., PACHECO, P. P., Narrandes, S., Wang, Y., & Xu, W. W. (2018) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20063 · Cited by 1,596
Using machine learning approaches for multi-omics data analysis: A review
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Machine learning and integrative analysis of biomedical big data
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Genotype-phenotype associations in Fanconi anemia: A literature review
Fiesco-Roa, M. Ó., Giri, N., McReynolds, L. J., Best, A. F., & Alter, B. P. (2019) · Blood Reviews · doi:10.1016/j.blre.2019.100589 · Cited by 198
Metadata
- Authors
- Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y.
- Affiliations
- Korea University (KR)
- Corresponding author
- Koo, Ja-Yong
- Venue
- BioData Mining
- Published
- 2017-01-26
- Volume
- 10
- Issue
- 1
- Pages
- 2-2
- ISSN
- 1756-0381 (ISSN-L)
- 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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