Cancer, SNPs, and Machine Learning
- Brett Poulin, Computing Science
- Duane Szafron, UofA CS
- Paul Lu, Department of Computing Science
- Russ Greiner, Dept of Computing Science; PI of AICML
- David S. Wishart, Departments of Computing Science and Biology, University of Alberta
- Brent Zanke
- Sambasivarao Damaraju, Cross Cancer Institute
- Thomas Kolacz
- Xiang Wan
Single nucleotide polymorphisms (SNPs) are genetic variations that may affect susceptibility to disease. We discuss the accuracy and efficiency of using various machine learning techniques with SNP data in order to distinguish between individuals who have breast cancer and those who do not.
Citation
B. Poulin, D. Szafron, P. Lu, R. Greiner, D. Wishart, B. Zanke, S. Damaraju, T. Kolacz, X. Wan. "Cancer, SNPs, and Machine Learning". Intelligent Systems for Molecular Biology (ISMB), Edmonton, Alberta, August 2002.| Keywords: | Cancer, SNP, PolyomX, machine learning |
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BibTeX
@incollection{Poulin+al:ISMB02,
author = {Brett Poulin and Duane Szafron and Paul Lu and Russ Greiner and
David S. Wishart and Brent Zanke and Sambasivarao Damaraju and Thomas
Kolacz and Xiang Wan},
title = {Cancer, SNPs, and Machine Learning},
booktitle = {Intelligent Systems for Molecular Biology (ISMB)},
year = 2002,
}Last Updated: June 06, 2007Submitted by Staurt H. Johnson
