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Review
. 2018 Jan-Feb;15(1):41-51.
doi: 10.21873/cgp.20063.

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

Shujun Huang  1   2 , Nianguang Cai  2 , Pedro Penzuti Pacheco  2 , Shavira Narrandes  2   3 , Yang Wang  4 , Wayne Xu  2   3   5
Affiliations
Review

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

Shujun Huang et al. Cancer Genomics Proteomics. 2018 Jan-Feb.

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: Machine learning (ML); biomarker discovery; cancer classification; classifier; driver gene; drug discovery; gene expression; gene selection; gene-gene interaction; genomics; kernel function; review; support vector machine (SVM).

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Figures

Figure 1
Figure 1. Linear SVM model. Two classes (red versus blue) were classified.
Figure 2
Figure 2. Kernel function. Data that cannot be separated by linear SVM can be transformed and separated by a kernel function.
Figure 3
Figure 3. Feature selection methods. Two frameworks (Feature filter and wrapper) were presented.
See this image and copyright information in PMC

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