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Studies in multidimensional data: E...

Scholtens, Denise Marie.

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Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership./
Author:	Scholtens, Denise Marie.
Description:	116 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0027.
Contained By:	Dissertation Abstracts International65-01B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3118003
ISBN:	0496654279

Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership.
Scholtens, Denise Marie.

Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership. - 116 p.

Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0027.

Thesis (Ph.D.)--Harvard University, 2004.

Statistical methodology for the analysis of multidimensional data of various forms is of tremendous importance in the current "information age". This thesis studies three different forms of multidimensional data, one relating to a long-standing statistical problem, and two resulting from recent bioinformatics investigations. While the three problems under consideration are quite different, the multidimensional nature of the data is of key importance.

ISBN: 0496654279Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership.
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100 1 $a Scholtens, Denise Marie. $3 1927943
245 1 0 $a Studies in multidimensional data: Estimation of the bivariate survival curve, analysis of factorial designed microarray experiments, identification of protein complex membership.
300 $a 116 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0027.
500 $a Adviser: Robert Gentleman.
502 $a Thesis (Ph.D.)--Harvard University, 2004.
520 $a Statistical methodology for the analysis of multidimensional data of various forms is of tremendous importance in the current "information age". This thesis studies three different forms of multidimensional data, one relating to a long-standing statistical problem, and two resulting from recent bioinformatics investigations. While the three problems under consideration are quite different, the multidimensional nature of the data is of key importance.
520 $a The first study is that of bivariate survival curve estimation, specifically related to safety and efficacy outcomes in clinical trials. We propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.
520 $a The second study focuses on the interpretation of gene expression data from factorial designed microarray experiments as they reflect genetic networks within the living, cellular environment. We discuss an analytic approach for framing biological questions in terms of statistical parameters to efficiently answer questions of interest using microarray data from factorial designed experiments. Investigators can extract pertinent and interpretable information from the data about the effects of the factors, their interactions with each other, and the statistical significance of these effects. By first examining how biological mechanisms are reflected in mRNA transcript abundance, investigators can better design microarray experiments to answer the most interesting questions.
520 $a The third study examines protein-protein interaction data and proposes a graph theoretic algorithm for protein complex membership estimation. Previous graph theoretic analyses have led to topological descriptions of the overall protein network. A more complete functional characterization of the cell demands additional methodology for the accurate, comprehensive identification of all complexes and their constituent proteins. Our algorithm accommodates both dynamic complex composition and multicomplex membership by individual proteins, two biological realities not accounted for in existing methods. The rigorous characterization of protein modules provides a platform for explicit hypothesis development regarding new protein complexes and complex interactivity.
590 $a School code: 0084.
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0308
710 2 0 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 65-01B.
790 1 0 $a Gentleman, Robert, $e advisor
790 $a 0084
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3118003