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Optimal design of experiments for emerging biological and computational applications

Ferhatosmanoglu, Nilgun
http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

Abstract Details

2007, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
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. The question addressed concerns how to deploy the samples to slides and select dye colors to improve 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. 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. The resulting “hybrid” designs constitute a compromise between the widely used “reference” designs and the “loop” designs. 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. The second type of application explored is the design of vector space search engines, which constitute perhaps the most common type of search technology in information retrieval. In this dissertation, two types of methods are explored separately and also combined to tune the selection of weights of the similarity distance function so that the search engine generates results of greater interest to users. The first type is so-called discrete choice analysis (DCA) methods to estimate the 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. 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.
Theodore Allen (Advisor)
Engineering, Industrial
design of experiments; discrete choice analysis; mixture experiments; bioinformatics; microarrays; information retrieval; search engines; relevance feedback

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Citations

  • Ferhatosmanoglu, N. (2007). Optimal design of experiments for emerging biological and computational applications [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

    APA Style (7th edition)

  • Ferhatosmanoglu, Nilgun. Optimal design of experiments for emerging biological and computational applications. 2007. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867.

    MLA Style (8th edition)

  • Ferhatosmanoglu, Nilgun. "Optimal design of experiments for emerging biological and computational applications." Doctoral dissertation, Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1179177867

    Chicago Manual of Style (17th edition)

osu1179177867
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© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.