東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Rank-based methods for statistical a...

The Johns Hopkins University.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Rank-based methods for statistical analysis of gene expression microarray data./
Author:	Lin, Xue.
Description:	115 p.
Notes:	Advisers: Donald Geman; Daniel Q. Nainman.
Contained By:	Dissertation Abstracts International69-12B.
Subject:	Biology, Biostatistics. -
Online resource:	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.
LDR:01579nam 2200289 a 45 001 854522
005 20100706
008 100706s2009 ||||||||||||||||| ||eng d
020 $a 9780549938460
035 $a (UMI)AAI3339873
035 $a AAI3339873
040 $a UMI $c UMI
100 1 $a Lin, Xue. $3 1020918
245 1 0 $a Rank-based methods for statistical analysis of gene expression microarray data.
300 $a 115 p.
500 $a Advisers: Donald Geman; Daniel Q. Nainman.
500 $a Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: 7593.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2009.
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.
590 $a School code: 0098.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 $a The Johns Hopkins University. $3 1017431
773 0 $t Dissertation Abstracts International $g 69-12B.
790 $a 0098
790 1 0 $a Geman, Donald, $e advisor
790 1 0 $a Nainman, Daniel Q., $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3339873