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 SHUJUN HUANG
A1 NIANGUANG CAI
A1 PEDRO PENZUTI PACHECO
A1 SHAVIRA NARRANDES
A1 YANG WANG
A1 WAYNE XU
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.