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Applications of Monte Carlo signal p...

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Applications of Monte Carlo signal processing in bioinformatics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applications of Monte Carlo signal processing in bioinformatics./
Author:	Liang, Kuo-ching.
Description:	159 p.
Notes:	Adviser: Xiaodong Wang.
Contained By:	Dissertation Abstracts International69-10B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3333390
ISBN:	9780549858508

Applications of Monte Carlo signal processing in bioinformatics.
Liang, Kuo-ching.

Applications of Monte Carlo signal processing in bioinformatics. - 159 p.

Adviser: Xiaodong Wang.

Thesis (Ph.D.)--Columbia University, 2008.

This thesis focuses on the application of Monte Carlo methods to solving problems in bioinformatics. More specifically, we want to be able to translate biological, genomic, and proteomic problems into mathematical problems by modeling them using discrete state-space hidden Markov models. Unknown parameters are modeled with prior distributions, and are estimated jointly with the unknown states. Depending on the type of problem, the model can also be formulated in a batched or sequential manner. We will show that in most cases, even when the problem is batched in nature, the sequential approach will provide better performance, and more efficient in terms of computational complexity. In this thesis, we will present applications of this methodology to problems such as DNA sequence base-calling, discovery of motifs in DNA sequences, phasing of haplotypes for diploid organisms, and the prediction of secondary structure for proteins based on amino acid sequences.

ISBN: 9780549858508Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Applications of Monte Carlo signal processing in bioinformatics.
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020 $a 9780549858508
035 $a (UMI)AAI3333390
035 $a AAI3333390
040 $a UMI $c UMI
100 1 $a Liang, Kuo-ching. $3 1029910
245 1 0 $a Applications of Monte Carlo signal processing in bioinformatics.
300 $a 159 p.
500 $a Adviser: Xiaodong Wang.
500 $a Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6314.
502 $a Thesis (Ph.D.)--Columbia University, 2008.
520 $a This thesis focuses on the application of Monte Carlo methods to solving problems in bioinformatics. More specifically, we want to be able to translate biological, genomic, and proteomic problems into mathematical problems by modeling them using discrete state-space hidden Markov models. Unknown parameters are modeled with prior distributions, and are estimated jointly with the unknown states. Depending on the type of problem, the model can also be formulated in a batched or sequential manner. We will show that in most cases, even when the problem is batched in nature, the sequential approach will provide better performance, and more efficient in terms of computational complexity. In this thesis, we will present applications of this methodology to problems such as DNA sequence base-calling, discovery of motifs in DNA sequences, phasing of haplotypes for diploid organisms, and the prediction of secondary structure for proteins based on amino acid sequences.
590 $a School code: 0054.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0715
710 2 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 69-10B.
790 $a 0054
790 1 0 $a Wang, Xiaodong, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3333390