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Sequential Monte Carlo and Dirichlet...

Logvinenko, Tanya.

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Sequential Monte Carlo and Dirichlet mixtures for extracting protein alignment models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sequential Monte Carlo and Dirichlet mixtures for extracting protein alignment models./
作者:	Logvinenko, Tanya.
面頁冊數:	125 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-11, Section: B, page: 5591.
Contained By:	Dissertation Abstracts International64-11B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3111752
ISBN:	0496592821

Sequential Monte Carlo and Dirichlet mixtures for extracting protein alignment models.
Logvinenko, Tanya.

Sequential Monte Carlo and Dirichlet mixtures for extracting protein alignment models. - 125 p.

Source: Dissertation Abstracts International, Volume: 64-11, Section: B, page: 5591.

Thesis (Ph.D.)--Stanford University, 2004.

In this dissertation we present various methods that can be used for aligning a pair of protein sequences or for finding similarities between multiple sequences. Commonly used non-Bayesian methods for aligning biological sequences often produce alignments which maximize some scoring function. However, the choice of the model parameters can strongly influence the resulting alignment. In addition, in the absence of a statistical model significance of the produced alignment can not be assessed. To address these issues we introduce formulation of the sequence comparison problem in Bayesian terms. Two Bayesian methods for aligning a pair of protein sequences are described and implemented. A rule for assessing significance of the resulting alignments is prescribed. For aligning multiple protein sequences a novel Bayesian method is proposed. Using Bayesian formulation of a problem and sequential Monte Carlo framework, the method progressively includes all sequences into the alignment. The resulting final alignment is improved by incorporating such Bayesian methodologies as Gibbs sampler and simulated annealing. Comparison study of the methods for biological sequence alignment (which uses the sets of protein sequences for which the true biological alignments are known) is presented. The novel Bayesian methods for pair-wise and multiple sequence alignment perform at least equivalent to or often better than the other methods for sequence alignment.

ISBN: 0496592821Subjects--Topical Terms:

517247
Statistics.

Sequential Monte Carlo and Dirichlet mixtures for extracting protein alignment models.
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520 $a In this dissertation we present various methods that can be used for aligning a pair of protein sequences or for finding similarities between multiple sequences. Commonly used non-Bayesian methods for aligning biological sequences often produce alignments which maximize some scoring function. However, the choice of the model parameters can strongly influence the resulting alignment. In addition, in the absence of a statistical model significance of the produced alignment can not be assessed. To address these issues we introduce formulation of the sequence comparison problem in Bayesian terms. Two Bayesian methods for aligning a pair of protein sequences are described and implemented. A rule for assessing significance of the resulting alignments is prescribed. For aligning multiple protein sequences a novel Bayesian method is proposed. Using Bayesian formulation of a problem and sequential Monte Carlo framework, the method progressively includes all sequences into the alignment. The resulting final alignment is improved by incorporating such Bayesian methodologies as Gibbs sampler and simulated annealing. Comparison study of the methods for biological sequence alignment (which uses the sets of protein sequences for which the true biological alignments are known) is presented. The novel Bayesian methods for pair-wise and multiple sequence alignment perform at least equivalent to or often better than the other methods for sequence alignment.
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