Abstract

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

Keywords

Support vector machineArtificial intelligenceComputer scienceGenomicsMachine learningEpigenomicsFeature (linguistics)Margin (machine learning)Computational biologyGenomeBiologyGeneGene expressionGeneticsDNA methylation

MeSH Terms

BiomarkersTumorDrug DiscoveryGenesNeoplasmGenomicsHumansNeoplasmsProtein Interaction MappingSupport Vector Machine

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Publication Info

Year
2018
Type
review
Volume
15
Issue
1
Pages
41-51
Citations
1489
Access
Closed

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Cite This

Shujun Huang, Nianguang Cai, P. Pacheco et al. (2018). Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics & Proteomics , 15 (1) , 41-51. https://doi.org/10.21873/cgp.20063

Identifiers

DOI
10.21873/cgp.20063
PMID
29275361
PMCID
PMC5822181

Data Quality

Data completeness: 90%