Advanced statistical modeling for phenotype prediction based on high throughput genomic data

Date
2015
Authors
Ma, Chifeng
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Abstract

The detection and prediction of phenotype is one of the primary goals of evolutionary genetics. This thesis focus on the application of advanced statistical modeling for phenotype detection and prediction as well as classification. In chapter two, the general algorithm for phenotype detection and prediction is surveyed. In chapter three, four and five, three application of advanced statistical modeling for phenotype prediction is demonstrated including a drug discovering study using connectivity map Microarray dataset; a cancer subtype classification study using TCGA and independent Microarray dataset; and a breast cancer differential methylation detection study. In chapter six, the conclusion is drowned and the future work is proposed.

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This item is available only to currently enrolled UTSA students, faculty or staff.
Keywords
High-throughput Genomic data, Phenotype Detection, Statistical Modeling
Citation
Department
Electrical and Computer Engineering