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Optimal design of experiments for em...

Ferhatosmanoglu, Nilgun.

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Optimal design of experiments for emerging biological and computational applications .

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimal design of experiments for emerging biological and computational applications ./
作者:	Ferhatosmanoglu, Nilgun.
面頁冊數:	165 p.
附註:	Adviser: Theodore T. Allen.
Contained By:	Dissertation Abstracts International68-05B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3264563
ISBN:	9780549028604

Optimal design of experiments for emerging biological and computational applications .
Ferhatosmanoglu, Nilgun.

Optimal design of experiments for emerging biological and computational applications . - 165 p.

Adviser: Theodore T. Allen.

Thesis (Ph.D.)--The Ohio State University, 2007.

This dissertation explores two types of applications of applied statistics techniques to develop methods associated with bioinformatics and information retrieval. The first type relates to planning probably the most common type of genetics related experiment, i.e., co-hybridized microarray testing. In these experiments, samples are paired and tested on specific slides using either red or green dyes. The question addressed concerns how to deploy the samples to slides and select dye colors in a manner that improves the sensitivity and specificity without increasing the associated cost. A generalized A-optimality criterion called the expected squared errors of coefficient estimates (ESECE) is proposed to aid in experimental design selection in this context. The proposed criterion also can be applied to any type of experimentation focused on parameter estimation. Heuristic methods to generate arrays using the proposed criterion are also suggested and the resulting so-called "hybrid" designs are described. It is revealed that the hybrid designs constitute a compromise between the "reference" designs used widely by practitioners and the "loop" designs explored in the literature. The proposed criterion and a study of 15,488 genes together suggest that reference designs are generally likely to foster more accurate estimation than loop designs. Also, the proposed "hybrid" designs likely offer further benefits in increased sensitivity and specificity with no added costs.

ISBN: 9780549028604Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Optimal design of experiments for emerging biological and computational applications .
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520 $a This dissertation explores two types of applications of applied statistics techniques to develop methods associated with bioinformatics and information retrieval. The first type relates to planning probably the most common type of genetics related experiment, i.e., co-hybridized microarray testing. In these experiments, samples are paired and tested on specific slides using either red or green dyes. The question addressed concerns how to deploy the samples to slides and select dye colors in a manner that improves the sensitivity and specificity without increasing the associated cost. A generalized A-optimality criterion called the expected squared errors of coefficient estimates (ESECE) is proposed to aid in experimental design selection in this context. The proposed criterion also can be applied to any type of experimentation focused on parameter estimation. Heuristic methods to generate arrays using the proposed criterion are also suggested and the resulting so-called "hybrid" designs are described. It is revealed that the hybrid designs constitute a compromise between the "reference" designs used widely by practitioners and the "loop" designs explored in the literature. The proposed criterion and a study of 15,488 genes together suggest that reference designs are generally likely to foster more accurate estimation than loop designs. Also, the proposed "hybrid" designs likely offer further benefits in increased sensitivity and specificity with no added costs.
520 $a The second type of application explored is the design of vector space search engines, which constitute perhaps the most common type of search engine. This research also relates to bioinformatics in that the development of information retrieval and database techniques for specific challenges related to biology is an active area of research. Contributions here focus on the selection of weights used in the distance function by the search engine. Documents are selected by calculating their weighted distance to the vector associated with the query. In this dissertation, two types of methods are explored separately and also combined in an attempt to tune the selection of weights so that the search engine generates results of greater interest to users. The first type is so-called discrete choice analysis (DCA) methods which permit the estimation of weights that putatively maximize the expected utility of users in the context of specific queries. The second type of method is the application of mixture modeling often associated with food science. Based on the fitting of specific types of mixture regression models, methods are proposed to enhance the expected user utility for a variety of queries. The DCA methods are illustrated using a news database and simulated users. The associated test problems provide an indication that the proposed methods could improve performance compared with the common strategy of applying equal weights for all semantic dimensions.
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