Statistical Analysis of Gene Expression Profile: Transcription Network Inference and Sample Classification

The copious information generated from transcriptomes gives us an opportunity to learn biological processes as integrated systems; however, due to numerous sources of variation, high dimensions of data structure, various levels of data quality, and different formats of the inputs, dissecting and interpreting such data presents daunting challenges to scientists. The goal of this research is to provide improved and new statistical tools for analyzing transcriptomes data to identify gene expression patterns for classifying samples, to discover regulatory gene networks using natural genetic perturbations, to develop statistical methods for model fitting and comparison of biochemical networks, and eventually to advance our capability to understand the principles of biological processes at the system level.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Genetics, Bioinformatics, and Computational Biology

Department dc:contributor.department

Genetics, Bioinformatics, and Computational Biology

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2004

Author dc:creator

• Bing, Nan

Chair dc:contributor.committeechair

• Hoeschele, Ina

Committee members dc:contributor.committeemember

• Ye, Keying
• Ramakrishnan, Naren
• Mendes, Pedro J. P.
• Saghai-Maroof, Mohammad A.

• Structural Equation Model
• Classification
• Mixture Model
• Gene Network
• Genetical Genomics
• Microarray

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-04192004-011825

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/11139