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Permanent link (DOI): https://doi.org/10.7939/R39S90

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USING SNP DATA TO PREDICT RADIATION TOXICITY FOR PROSTATE CANCER PATIENTS Open Access

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Other title
Subject/Keyword
SNP, radiation toxicity, machine learing, classification
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Mirzazadeh, Farzaneh
Supervisor and department
Examining committee member and department
Department
Department of Computing Science
Specialization

Date accepted
2010-02-02T21:24:31Z
Graduation date
2010-06
Degree
Master of Science
Degree level
Master's
Abstract
Radiotherapy is often used to treat prostate cancer. While using high dose of radiation does kill cancer cells, it can cause toxicity in healthy tissues for some patients. It would be best to apply this treatment only to patients who are likely to be immune from such toxicity. This requires a classifier that can predict, before treatment, which patients are likely to exhibit severe toxicity. Here, we explore ways to use certain genetic features, called Single Nucleotide Polymorphisms (SNPs), for this task. This thesis uses several machine learning methods for learning such classifiers for predicting toxicity. This problem is challenging as there are a large number of features (164,273 SNPs) but only 82 samples. We explore an ensemble classification method for this problem, called Mixture Using Variance (MUV), which first learns several different base probabilistic classifiers, then for each query combines the responses of the different base classifiers based on their respective variances. The original MUV learns the individual classifiers using bootstrap sampling of the training data; we modify this by considering different subsets of the features for each classifier. We derive a new combination rule for base classifiers in the proposed setting and obtain some new theoretical results. Based on characteristics of our task, we propose an approach that involves first clustering the features before selecting only a subset of features from each cluster for each base classifier. Unfortunately, we were unable to predict radiation toxicity in prostate cancer patients using just the SNP values. However, our further experimental results reveal strong relation between correctness of a classifier in its prediction and the variance of the response to the corresponding classification query, which show that the main idea is promising.
Language
English
DOI
doi:10.7939/R39S90
Rights
License granted by Farzaneh Mirzazadeh (mirzazad@ualberta.ca) on 2010-02-01T22:15:28Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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