RT Journal Article SR Electronic T1 Applications of Support Vector Machine (SVM) Learning in Cancer Genomics JF Cancer Genomics - Proteomics JO Cancer Genomics Proteomics FD International Institute of Anticancer Research SP 41 OP 51 VO 15 IS 1 A1 HUANG, SHUJUN A1 CAI, NIANGUANG A1 PACHECO, PEDRO PENZUTI A1 NARRANDES, SHAVIRA A1 WANG, YANG A1 XU, WAYNE YR 2018 UL http://cgp.iiarjournals.org/content/15/1/41.abstract 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.