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

SHUJUN HUANG, NIANGUANG CAI, PEDRO PENZUTI PACHECO, SHAVIRA NARRANDES, YANG WANG and WAYNE XU
Cancer Genomics & Proteomics January 2018, 15 (1) 41-51;
SHUJUN HUANG
1College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
2Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada
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NIANGUANG CAI
2Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada
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PEDRO PENZUTI PACHECO
2Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada
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SHAVIRA NARRANDES
2Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada
3Departments of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
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YANG WANG
4Department of Computer Science, Faculty of Sciences, University of Manitoba, Winnipeg, Canada
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WAYNE XU
1College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
2Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada
3Departments of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
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  • For correspondence: wayne.xu@umanitoba.ca
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    Figure 1.

    Linear SVM model. Two classes (red versus blue) were classified.

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    Figure 2.

    Kernel function. Data that cannot be separated by linear SVM can be transformed and separated by a kernel function.

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    Figure 3.

    Feature selection methods. Two frameworks (Feature filter and wrapper) were presented.

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Cancer Genomics - Proteomics: 15 (1)
Cancer Genomics & Proteomics
Vol. 15, Issue 1
January-February 2018
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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
SHUJUN HUANG, NIANGUANG CAI, PEDRO PENZUTI PACHECO, SHAVIRA NARRANDES, YANG WANG, WAYNE XU
Cancer Genomics & Proteomics Jan 2018, 15 (1) 41-51;

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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
SHUJUN HUANG, NIANGUANG CAI, PEDRO PENZUTI PACHECO, SHAVIRA NARRANDES, YANG WANG, WAYNE XU
Cancer Genomics & Proteomics Jan 2018, 15 (1) 41-51;
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Keywords

  • Machine learning (ML)
  • support vector machine (SVM)
  • classifier
  • Genomics
  • kernel function
  • gene expression
  • cancer classification
  • gene selection
  • biomarker discovery
  • drug discovery
  • driver gene
  • gene-gene interaction
  • review
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