A major goal of human genetics is the discovery and validation of genetic polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic models. This etiological complexity, coupled with rapid advances in genotyping technology present enormous theoretical and practical concerns for statistical and computational analysis. Specifically, the challenge presented by epistasis, or gene-gene interactions, has sparked the development of a multitude of statistical techniques over the years. Subsequently, pattern matching and machine learning approaches have been explored to overcome the limitations of traditional computational methods. Grammatical Evolution Neural Networks (GENN) uses grammatical evolution to optimize neural network architectures and better detect and analyze gene-gene interactions. Motivated by good results shown by GENN to identify epistasis in complex datasets, we have developed a new method of Grammatical Evolution Decision Trees (GEDT). GEDT replaces the black-box approach of neural networks with the white-box approach of decision trees improving understandability and interpretability. We provide a detailed technical understanding of coupling Grammatical Evolution with Decision Tress using Backus Naur Form (BNF) grammar. Further, the GEDT system has been analyzed for power results on simulated datasets. Finally, we show the results of using GEDT on two different epistatis models and discuss the direction it would take in the future.
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-10302009-181439 |
| Date | 30 November 2009 |
| Creators | Deodhar, Sushamna Shriniwas |
| Contributors | Dr. Alison Motsinger-Reif, Dr. Stefen Heber, Dr. Alan L. Tharp |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-10302009-181439/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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