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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.