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Estimating the Overlap of Top Instances in Lists Ranked by Correlation to Label Open Access


Other title
Genome wide association studies
Gene signatures
Machine Learning
Type of item
Degree grantor
University of Alberta
Author or creator
Damavandi, Babak
Supervisor and department
Russell Greiner (Computing Science)
Examining committee member and department
Csaba Szepesvari (Computing Science)
Sambasivarao Damaraju (Cross Cancer Institute)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown that most such rankings vary considerably across different studies, even in the case of sub- sampling from a single dataset. This motivates our interest in formally investigating the overlap of the top ranked features in two lists sorted by correlation with an outcome. This dissertation presents a mathematical model for better understanding lists whose entries are ranked by Pearson correlation coefficient with an outcome. We show that our model is able to accurately predict the expected overlap between two ranked lists based on reasonable assumptions. We also discuss how to generalize this model to find the overlap between other forms of rankings, provided that they satisfy mild assumptions.
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 these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before 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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