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Bayesian Approach for Two Model-sele...

Liang, Tong.

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Bayesian Approach for Two Model-selection-related Bioinformatics Problems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian Approach for Two Model-selection-related Bioinformatics Problems./
Author:	Liang, Tong.
Description:	143 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3576377
ISBN:	9781303561658

Bayesian Approach for Two Model-selection-related Bioinformatics Problems.
Liang, Tong.

Bayesian Approach for Two Model-selection-related Bioinformatics Problems. - 143 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.

Bayesian approach is a powerful framework for inferring the parameters and structures of complicated probabilistic models from data. It is widely applied in many areas and also ideal for Bioinformatics problems due to their usually high complexity. In this thesis, new Bayesian models and computing methods are introduced to solve two Bioinformatics problems which are both related to model selection. The first problem is about the repeat pattern recognition. Tandem repeats occur frequently in DNA sequences. They are important for studying genome evolution and human disease. This thesis focuses on the case that an unknown number of tandem repeat segments of the same pattern are dispersively distributed in a sequence. A probabilistic generative model is introduced for the tandem repeats. Markov chain Monte Carlo algorithms are used to explore the posterior distribution as an effort to infer both the specific pattern of the tandem repeats and the location of repeat segments. Furthermore, reversible jump Markov chain Monte Carlo algorithms are used to address the transdimensional model selection problem raised by the variable number of repeat segments. The second part of this thesis is engaged in the conformational transitions of biomolecules. Because the function of a biological biomolecule is inherently related to its variable conformations which can be grouped into a set of metastable or long-live states, conformational transitions are important in biological processes. The 3D structure changes are generally simulated from the molecular dynamics computer simulation. Based on the conformational transitions on microstate level from molecular dynamics simulation, a Bayesian approach is developed to cluster the microstates into an uncertainty number of metastable that induces the model selection problem. With these two problems, this thesis shows that the Bayesian approach for bioinformatics problems has its advantages in terms of taking account of the inherent uncertainty in biological data, handling noisy or missing data, and dealing with the model selection problem.

ISBN: 9781303561658Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian Approach for Two Model-selection-related Bioinformatics Problems.
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100 1 $a Liang, Tong. $3 2102249
245 1 0 $a Bayesian Approach for Two Model-selection-related Bioinformatics Problems.
300 $a 143 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Adviser: Shuo-Yen Li.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.
520 $a Bayesian approach is a powerful framework for inferring the parameters and structures of complicated probabilistic models from data. It is widely applied in many areas and also ideal for Bioinformatics problems due to their usually high complexity. In this thesis, new Bayesian models and computing methods are introduced to solve two Bioinformatics problems which are both related to model selection. The first problem is about the repeat pattern recognition. Tandem repeats occur frequently in DNA sequences. They are important for studying genome evolution and human disease. This thesis focuses on the case that an unknown number of tandem repeat segments of the same pattern are dispersively distributed in a sequence. A probabilistic generative model is introduced for the tandem repeats. Markov chain Monte Carlo algorithms are used to explore the posterior distribution as an effort to infer both the specific pattern of the tandem repeats and the location of repeat segments. Furthermore, reversible jump Markov chain Monte Carlo algorithms are used to address the transdimensional model selection problem raised by the variable number of repeat segments. The second part of this thesis is engaged in the conformational transitions of biomolecules. Because the function of a biological biomolecule is inherently related to its variable conformations which can be grouped into a set of metastable or long-live states, conformational transitions are important in biological processes. The 3D structure changes are generally simulated from the molecular dynamics computer simulation. Based on the conformational transitions on microstate level from molecular dynamics simulation, a Bayesian approach is developed to cluster the microstates into an uncertainty number of metastable that induces the model selection problem. With these two problems, this thesis shows that the Bayesian approach for bioinformatics problems has its advantages in terms of taking account of the inherent uncertainty in biological data, handling noisy or missing data, and dealing with the model selection problem.
590 $a School code: 1307.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Information Technology. $3 1030799
650 4 $a Biology, Bioinformatics. $3 1018415
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710 2 $a The Chinese University of Hong Kong (Hong Kong). $3 1017547
773 0 $t Dissertation Abstracts International $g 75-03B(E).
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793 $a English
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