Title page for ETD etd-050807-040544

Document Type thesis

Author Name Thakkar, Dharmesh

Email Address dharmesh.thakkar at gmail.com

URN etd-050807-040544

Title Hypothesis-Driven Specialization-based Analysis of Gene Expression Association Rules

Degree MS

Department Computer Science

Advisors
Prof. Carolina Ruiz, Advisor
Prof. Liz Ryder, Co-Advisor
Prof. Murali Mani, Reader

Keywords
bioinformatics
gene expression
association rules
data mining

Date of Presentation/Defense 2007-05-03

Availability unrestricted

Abstract
During the development of many diseases such as cancer and diabetes, the pattern of gene expression within certain cells changes. A vital part of understanding these diseases will come from understanding the factors governing gene expression. This thesis work focused on mining association rules in the context of gene expression. We designed and developed a tool that enables domain experts to interactively analyze association rules that describe relationships in genetic data. Association rules in their native form deal with sets of items and associations among them. But domain experts hypothesize that additional factors like relative ordering and spacing of these items are important aspects governing gene expression.
We proposed hypothesis-based specializations of association rules to identify biologically significant relationships. Our approach also alleviates the limitations inherent in the conventional association rule mining that uses a support-confidence framework by providing filtering and reordering of association rules according to other measures of interestingness in addition to support and confidence. Our tool supports visualization of genetic data in the context of a rule, which facilitates rule analysis and rule specialization. The improvement in different measures of interestingness (e.g., confidence, lift, and p-value) enabled by our approach is used to evaluate the significance of the specialized rules.

Files
dthakkar.pdf

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Document Type	thesis
Author Name	Thakkar, Dharmesh
Email Address	dharmesh.thakkar at gmail.com
URN	etd-050807-040544
Title	Hypothesis-Driven Specialization-based Analysis of Gene Expression Association Rules
Degree	MS
Department	Computer Science
Advisors	Prof. Carolina Ruiz, Advisor Prof. Liz Ryder, Co-Advisor Prof. Murali Mani, Reader
Keywords	bioinformatics gene expression association rules data mining
Date of Presentation/Defense	2007-05-03
Availability	unrestricted
Abstract During the development of many diseases such as cancer and diabetes, the pattern of gene expression within certain cells changes. A vital part of understanding these diseases will come from understanding the factors governing gene expression. This thesis work focused on mining association rules in the context of gene expression. We designed and developed a tool that enables domain experts to interactively analyze association rules that describe relationships in genetic data. Association rules in their native form deal with sets of items and associations among them. But domain experts hypothesize that additional factors like relative ordering and spacing of these items are important aspects governing gene expression. We proposed hypothesis-based specializations of association rules to identify biologically significant relationships. Our approach also alleviates the limitations inherent in the conventional association rule mining that uses a support-confidence framework by providing filtering and reordering of association rules according to other measures of interestingness in addition to support and confidence. Our tool supports visualization of genetic data in the context of a rule, which facilitates rule analysis and rule specialization. The improvement in different measures of interestingness (e.g., confidence, lift, and p-value) enabled by our approach is used to evaluate the significance of the specialized rules.
Files	dthakkar.pdf