Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
akron1302619630.pdf (1.35 MB)
ETD Abstract Container
Abstract Header
Predicting Gene Relations Using Bayesian Networks
Author Info
Sriram, Aparna
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=akron1302619630
Abstract Details
Year and Degree
2011, Master of Science, University of Akron, Computer Science.
Abstract
Genes are the biological units responsible for the hereditary characteristics in living organisms. It takes years of arduous research to predict relations among genes using biological experiments. An alternate method that is quicker and simpler can prove to be of great significance. This research focuses on exploring a solution in the form of Bayesian Networks and its application to predict gene relations from microarray experiments. Biological pathways are used to depict the interactions among genes during biological process. In this study, such pathways were selected from the EcoCyc database for the bacterium strain EColi MG1655. For the genes involved in the pathways, the microarray data of seven experiments was obtained from the Many Microbe Microarray Database. The datasets were used for building the directed acyclic graphs using Bayesian Networks. Directed acyclic graphs (DAGs) were obtained for each experiment with a set of topological orders. A union graph was constructed based on the frequency of occurrence of each edge in the DAGs obtained. A final consensus graph was obtained by selecting a threshold frequency for the edges. A comparison of the resultant consensus graph to the biological pathway indicates that the existing gene relations can be replicated to major extent. In addition to the existing gene relations described in the pathways, a few other edges were also found to have quite a high frequency of occurrence. Previous researches based on biological experimental studies confirmed several gene relations that were revealed by these edges. The results indicate that Bayesian networks can play a vital role not only in confirming the already existing gene relations, but also in predicting the possible gene interactions.
Committee
Zhong-Hui Duan, Dr. (Advisor)
Chien-Chung Chan, Dr. (Committee Member)
Yingcai Xiao, Dr. (Committee Member)
Pages
63 p.
Subject Headings
Bioinformatics
;
Computer Science
Keywords
Gene relations
;
bayesian networks
;
pathways
;
microarray
;
bioinformatics
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Sriram, A. (2011).
Predicting Gene Relations Using Bayesian Networks
[Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302619630
APA Style (7th edition)
Sriram, Aparna.
Predicting Gene Relations Using Bayesian Networks.
2011. University of Akron, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=akron1302619630.
MLA Style (8th edition)
Sriram, Aparna. "Predicting Gene Relations Using Bayesian Networks." Master's thesis, University of Akron, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302619630
Chicago Manual of Style (17th edition)
Abstract Footer
Document number:
akron1302619630
Download Count:
830
Copyright Info
© 2011, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.
Release 3.2.12