Predicting cytotoxicity is a challenging task because of the complex biological mechanisms behind it. Cytotoxicity due to toxin, biologically produced poison, is known to play a substantial role in disease process. Two objectives in this research are to derive robust general predictive cytotoxicity models to minimize unnecessary toxicity. The first objective is to build novel predictive statistical models for cytotoxicity data based on lymphoblastoid cell lines obtained from in vitro studies. This might be an important step for accomplishing a goal in biomedical/biophamarceutical research, by obtaining the best medical outcomes by minimizing toxicity in regard to a person’s genomic profile. The second objective is to build predictive models to predict population-level cytotoxicity for unknown compounds based on chemical structural features. Since environmental chemical compounds has greatly influence on human health, the predictive statistical models built within this objective could be helpful to government agencies for the relevant decision-making.
|Commitee:||Kim-Park, Yong Hee, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 55/02M(E), Masters Abstracts International|
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