Author's School

School of Engineering & Applied Science

Author's Department/Program

Computer Science and Engineering


English (en)

Date of Award

January 2011

Degree Type


Degree Name

Master of Arts (MA)

Chair and Committee

S. Swamidass


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.


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