PolyomX: Cancer, SNPs, and Machine Learning

• Author(s) / Creator(s)

  • Poulin, Brett
  • Wan, Xiang
  • Kolacz, Tom

• Technical report TR06-03. Single nucleotide polymorphisms (SNPs) are genetic markers that may be used to identify the causes and risks of cancer. The sheer volume of data generated by SNP studies is difficult to analyze by hand. Machine learning techniques have been developed to address the types of data and the sizes of data sets provided by these studies in an efficient matter. We discuss the applicability of 5 machine learning techniques to the classification of cancer patients using SNP data. The techniques include decision trees, naive Bayes, neural networks, support vector machines, and clustering methods. | TRID-ID TR06-03

• Date created

  2006

• Subjects / Keywords

  • Machine Learning

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3F47X

• License

  Attribution 3.0 International