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Periodicity, change detection and pr...

The University of Western Ontario (Canada).

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Periodicity, change detection and prediction in microarrays.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Periodicity, change detection and prediction in microarrays./
Author:	Islam, Mohammad Shahidul.
Description:	102 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 2701.
Contained By:	Dissertation Abstracts International69-05B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=NR39284
ISBN:	9780494392843

Periodicity, change detection and prediction in microarrays.
Islam, Mohammad Shahidul.

Periodicity, change detection and prediction in microarrays. - 102 p.

Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 2701.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 2008.

KEYWORDS: Beowulf cluster computing with R, change points in array CGH, class prediction in microarray datasets, detection of periodicity, nearest neighbour covariates, wavelet change-point detection.

ISBN: 9780494392843Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Periodicity, change detection and prediction in microarrays.
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020 $a 9780494392843
035 $a (UMI)AAINR39284
035 $a AAINR39284
040 $a UMI $c UMI
100 1 $a Islam, Mohammad Shahidul. $3 1029899
245 1 0 $a Periodicity, change detection and prediction in microarrays.
300 $a 102 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 2701.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 2008.
520 $a KEYWORDS: Beowulf cluster computing with R, change points in array CGH, class prediction in microarray datasets, detection of periodicity, nearest neighbour covariates, wavelet change-point detection.
520 $a Three topics in the analysis of microarray genomic data are discussed and improved statistical methods are developed in each case.
520 $a A statistical test with higher power is developed for detecting periodicity in microarray time series data. Periodicity in short series, with non-Fourier frequencies, is detected through a Pearson curve calibrated to the null distribution obtained by computer simulation. This method is applicable even in the presence of missing values or unequal time intervals. The usefulness of the new method is demonstrated on simulated series as well as actual microarray time series.
520 $a The second topic develops a new method for detection of changes in DNA or gene copy number. Regions for DNA copy number aberrations in chromosomal material are detected using maximum overlap discrete wavelet transform (MODWT). It is shown how repeated application of MODWT to a series can be used to confirm the presence of change points. Application to simulated as well as array CGH (Comparative Genomic Hybridization) data confirms the excellent performance of this method.
520 $a In the third topic, an improved class predictor for tissue samples in microarray experiments is developed by incorporating nearest neighbour covariates (NNC). It is demonstrated that this method reduces the mis-classification errors in both simulated and actual microarray data.
590 $a School code: 0784.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
690 $a 0715
710 2 $a The University of Western Ontario (Canada). $3 1017622
773 0 $t Dissertation Abstracts International $g 69-05B.
790 $a 0784
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=NR39284