PT  - JOURNAL ARTICLE
AU  - SHUJUN HUANG
AU  - NIANGUANG CAI
AU  - PEDRO PENZUTI PACHECO
AU  - SHAVIRA NARRANDES
AU  - YANG WANG
AU  - WAYNE XU
TI  - Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
DP  - 2018 Jan 01
TA  - Cancer Genomics - Proteomics
PG  - 41--51
VI  - 15
IP  - 1
4099  - http://cgp.iiarjournals.org/content/15/1/41.short
4100  - http://cgp.iiarjournals.org/content/15/1/41.full
SO  - Cancer Genomics Proteomics2018 Jan 01; 15
AB  - 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.