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Rank-based methods for statistical a...

The Johns Hopkins University.

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Rank-based methods for statistical analysis of gene expression microarray data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Rank-based methods for statistical analysis of gene expression microarray data./
作者:	Lin, Xue.
面頁冊數:	115 p.
附註:	Advisers: Donald Geman; Daniel Q. Nainman.
Contained By:	Dissertation Abstracts International69-12B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3339873
ISBN:	9780549938460

Rank-based methods for statistical analysis of gene expression microarray data.
Lin, Xue.

Rank-based methods for statistical analysis of gene expression microarray data. - 115 p.

Advisers: Donald Geman; Daniel Q. Nainman.

Thesis (Ph.D.)--The Johns Hopkins University, 2009.

Gene expression microarray data have great potential in helping researchers to understand the biological mechanisms of disease and hence their diagnosis. How to utilize and analyze these large-scale data to extract useful information is the major challenge of bioinformatics field. In this dissertation, we propose a rank-based framework for the statistical analysis of expression microarray data. We first explore the rank-invariant property of various microarray preprocessing methods, then propose a rank-based classifier called Top-scoring Triplet (TST), and finally we present a maximum entropy model of distribution on ranks.

ISBN: 9780549938460Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Rank-based methods for statistical analysis of gene expression microarray data.
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520 $a Gene expression microarray data have great potential in helping researchers to understand the biological mechanisms of disease and hence their diagnosis. How to utilize and analyze these large-scale data to extract useful information is the major challenge of bioinformatics field. In this dissertation, we propose a rank-based framework for the statistical analysis of expression microarray data. We first explore the rank-invariant property of various microarray preprocessing methods, then propose a rank-based classifier called Top-scoring Triplet (TST), and finally we present a maximum entropy model of distribution on ranks.
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