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Permanent link (DOI): https://doi.org/10.7939/R3F47X

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PolyomX: Cancer, SNPs, and Machine Learning Open Access

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Author or creator
Wan, Xiang
Poulin, Brett
Kolacz, Tom
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Subject/Keyword
Machine Learning
Type of item
Computing Science Technical Report
Computing science technical report ID
TR06-03
Language
English
Place
Time
Description
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.
Date created
2006
DOI
doi:10.7939/R3F47X
License information
Creative Commons Attribution 3.0 Unported
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