Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

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...

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Bibliographic Details
Published inCancer genomics & proteomics Vol. 15; no. 1; pp. 41 - 51
Main Authors Huang, Shujun, Cai, Nianguang, Pacheco, Pedro Penzuti, Narrandes, Shavira, Wang, Yang, Xu, Wayne
Format Journal Article
LanguageEnglish
Published Greece International Institute of Anticancer Research 01.01.2018
Subjects
Biomarkers, Tumor
Drug Discovery
Genes, Neoplasm
Genomics
Humans
Neoplasms - classification
Neoplasms - drug therapy
Neoplasms - genetics
Protein Interaction Mapping
Review
Support Vector Machine
gene selection
support vector machine (SVM)
Machine learning (ML)
cancer classification
kernel function
biomarker discovery
drug discovery
review
classifier
genomics
driver gene
gene-gene interaction
gene expression
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Summary: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.
ISSN:1109-6535
1790-6245
DOI:10.21873/cgp.20063