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Genomic data analysis and processing...

Su, Shih-Chieh.

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Genomic data analysis and processing with signal processing techniques.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Genomic data analysis and processing with signal processing techniques./
Author:	Su, Shih-Chieh.
Description:	191 p.
Notes:	Advisers: C.-C. Jay Kuo; Ting Chen.
Contained By:	Dissertation Abstracts International67-10B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3238321
ISBN:	9780542927966

Genomic data analysis and processing with signal processing techniques.
Su, Shih-Chieh.

Genomic data analysis and processing with signal processing techniques. - 191 p.

Advisers: C.-C. Jay Kuo; Ting Chen.

Thesis (Ph.D.)--University of Southern California, 2006.

Bioinformatics is an emerging multi-disciplinary field. In this research, we study two problems originating from biological applications using signal processing and statistical patter recognition techniques: (1) the genomic sequence alignment problem, and (2) the data integrity problem on Single Nucleotide Polymorphisms (SNP) data sets.

ISBN: 9780542927966Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Genomic data analysis and processing with signal processing techniques.
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020 $a 9780542927966
035 $a (UMI)AAI3238321
035 $a AAI3238321
040 $a UMI $c UMI
100 1 $a Su, Shih-Chieh. $3 1271246
245 1 0 $a Genomic data analysis and processing with signal processing techniques.
300 $a 191 p.
500 $a Advisers: C.-C. Jay Kuo; Ting Chen.
500 $a Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5966.
502 $a Thesis (Ph.D.)--University of Southern California, 2006.
520 $a Bioinformatics is an emerging multi-disciplinary field. In this research, we study two problems originating from biological applications using signal processing and statistical patter recognition techniques: (1) the genomic sequence alignment problem, and (2) the data integrity problem on Single Nucleotide Polymorphisms (SNP) data sets.
520 $a First, we introduced a gap sequence matching technique, which facilitates the match of genomic sequences. We studied the behavior of these gap sequences and proposed two methods, histogram-aided alignment (HAA) and matched filter alignment (MFA), to perform the alignment for pairwise and multiple genomic sequences. The main contribution here is that, even with partial knowledge of a genomic sequence (namely, the gap structure), we are able to accurately predict the remaining portions of the sequence using arguments from information theory. We proposed a fast gap sequence alignment system with suffix array implementation (GSA-SA). This system outperforms the current BLAST system (build 2.2.13) in terms of time and accuracy. Since BLAST is the most widely used system for sequence alignment, we expect GSA-SA to facilitate the sequence alignment technique in the near future.
520 $a Next, the problem of haplotype block partitioning and missing SNP inference was studied. We proposed to measure the haplotype diversity inside a block using the entropy. Based on this measure, we developed a new algorithm, called IPI (iterative partitioning-inference). The IPI algorithm consists of two steps. In the first step, a dynamic programming algorithm is adopted to partition haplotype data into blocks to minimize the total block entropy. In the second step, an EM-like algorithm is used to infer missing SNPs in each haplotype block to minimize the local block diversity. The IPI algorithm iterates these two steps until the total block entropy is minimized. It was shown by experimental results that the global IPI approach significantly improves the accuracy of the inference. Then, we considered the block-free framework that can accommodate larger data sets for missing SNP inference, without partitioning the haplotype block. The block-free inference system can be extended to haplotype inference and missing genotype inference. Our developed systems can infer all kinds of uncertain data from available data sets.
590 $a School code: 0208.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0308
690 $a 0369
690 $a 0544
690 $a 0715
710 2 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 67-10B.
790 $a 0208
790 1 0 $a Chen, Ting, $e advisor
790 1 0 $a Kuo, C.-C. Jay, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3238321