Date of Award

Spring 1-1-2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Debra Goldberg

Second Advisor

Katerina Kechris

Third Advisor

Lawrence Hunter

Abstract

In this project, I present approaches to rank Transcription Factors (TFs) based on their direct and indirect regulation of a given set of genes. I present two methods: the first is a classical test of overlap approach, while the second uses a network flow algorithm. Moreover, I present a novel randomization approach based on a non-uniform background model to which the results of each method are compared to produce a more robust ranking. The proposed methods are applied on data sets associated with the Mediator protein complex where we aimed to identify the different TFs that are primarily responsible for causing the variations in gene expression when two of the Mediator’s proteins CDK8 and CDK19 are knocked down. The results showed different TFs rankings between the different proteins. My ranking system will be useful for nominating the TFs to be tested experimentally by biologists to certify the main TFs responsible for the difference in gene expression.

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