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

hypothesis test;statistics;data mining;chemical mixtures;nonlinear model;QSAR

Zhang, Ke ; Dr. Cavell Brownie, Committee Member,Dr. David A. Dickey, Committee Member,Dr. Jacqueline M. Hughes-Oliver, Committee Chair,Dr. Sidney Stanley Young, Committee Member,Zhang, Ke ; Dr. Cavell Brownie ; Committee Member ; Dr. David A. Dickey ; Committee Member ; Dr. Jacqueline M. Hughes-Oliver ; Committee Chair ; Dr. Sidney Stanley Young ; Committee Member

University:North Carolina State University

关键词: hypothesis test;    statistics;    data mining;    chemical mixtures;    nonlinear model;    QSAR;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5560/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

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.