A novel computational method for predicting tissue-specific disease-associated signaling pathways in human utilizing Caenorhabditis elegans reference data.

Signal transduction is a hot topic as molecular biology grows because it directly relates to cellular processes, supporting function of the organism as a whole. A dysfunctional signal transduction will cause uncoordinated cellular behaviors. For humans, these uncoordinated cellular behaviors will cause diseases. To study mechanism of signal transduction, diverse approaches have been applied, including traditional experimental and computational methods. Compared to traditional experimental approaches, computational methods are better in analyzing large amounts of data and predicting results from limited data. In this research, a novel computational method is built to predict tissue-specific disease-associated signaling pathways in human by referring to C. elegans data. Tissue-specificity and disease association data are utilized to perform this prediction, with a support of a novel pathway finding algorithm. Lists of candidate pathways associated with certain selected diseases are successfully generated from the results.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Grantor

Baylor University.

Year dc:date.issued

2013

Author dc:creator

• Peng, Xuan, 1989-

Advisors dc:contributor.advisor

• Lee, Myeongwoo.
• Cho, Young-Rae.

• Signaling transduction.
• Pathway prediction.
• C. elegans.
• Tissue specificity.
• Disease-associated genes.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/8851

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/8851