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

Journal: Cancer genomics & proteomics
Published Date: Jan 1, 2018

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.

Authors

  • Shujun Huang
    College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
  • Nianguang Cai
    Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.
  • Pedro Penzuti Pacheco
    Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.
  • Shavira Narrandes
    Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Wayne Xu
    Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.

Keywords

External Resources

Popular Topics
Recent Journals