Author's School

School of Engineering & Applied Science

Author's Department/Program

Computer Science and Engineering

Language

English (en)

Date of Award

January 2011

Degree Type

Thesis

Degree Name

Master of Arts (MA)

Chair and Committee

S. Swamidass

Abstract

Copy Number Variations: CNVs) are a significant source of human genetic diversity and are believed to be responsible for a wide variety of phenotypic variation. Recent advances in microarray-based genomic hybridization techniques have facilitated CNV analysis as a viable diagnostic technique in the clinic, and several public databases of well-characterized CNVs are being compiled, but a standard for interpreting uncharacterized CNVs has yet to emerge. This thesis examines the clinical interpretation of uncharacterized CNVs as a multiple instance binary classification problem. We analyze the current state of clinical techniques, then present and test several novel statistical approaches to the problem.

DOI

https://doi.org/10.7936/K7CR5RDM

Comments

Permanent URL: http://dx.doi.org/10.7936/K7CR5RDM

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