Applications of Support Vector Machine (SVM) Learning in Cancer Genomics (original) (raw)
Review ArticleR
Open Access
Cancer Genomics & Proteomics January 2018, 15 (1) 41-51;
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.
- Machine learning (ML)
- support vector machine (SVM)
- classifier
- genomics
- kernel function
- gene expression
- cancer classification
- gene selection
- biomarker discovery
- drug discovery
- driver gene
- gene-gene interaction
- review
- Received September 7, 2017.
- Revision received October 3, 2017.
- Accepted October 23, 2017.
- Copyright © 2018 The Author(s). Published by the International Institute of Anticancer Research.