Statistical Analysis of Compounds Using OBSTree and Compound Mixtures Using Nonlinear Models

Show full item record

Title: Statistical Analysis of Compounds Using OBSTree and Compound Mixtures Using Nonlinear Models
Author: Zhang, Ke
Advisors: Dr. Cavell Brownie, Committee Member
Dr. David A. Dickey, Committee Member
Dr. Jacqueline M. Hughes-Oliver, Committee Chair
Dr. Sidney Stanley Young, Committee Member
Abstract: A novel tree-structured data-mining tool is proposed to automatically search for and find high performance classification and important quantitative structure-activity relationships (QSARs) hidden in large data sets. The presence or absence of multiple chemical features is implemented to identify more informative splitting rules. A stochastic optimization scheme combined with a new splitting criterion and a post-trimming procedure is developed to find global optimum splitting variables. The algorithm is also ready to serve as a powerful predictive tool for estimating unknown biological activities according to the chemical structures. We also investigate several statistical issues in chemical mixture studies. With a thorough review of different concepts of additivity the criteria for evaluating a concept of additivity are discussed and a particular concept of additivity is generalized to some complicated studies. A nonlinear dose-response model is initially developed for binary mixtures. The model can be easily generalized to a mixture of $M$ chemicals. Different types of test statistics under multiplicity adjustments are proposed to test the interactions.
Date: 2007-01-28
Degree: PhD
Discipline: Statistics
URI: http://www.lib.ncsu.edu/resolver/1840.16/5560


Files in this item

Files Size Format View
etd.pdf 1.284Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record