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Model-based Inference of Gene Regula...

Wang, Yuanfeng.

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Model-based Inference of Gene Regulatory Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Model-based Inference of Gene Regulatory Networks./
Author:	Wang, Yuanfeng.
Description:	154 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
Contained By:	Dissertation Abstracts International74-07B(E).
Subject:	Biophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3557247
ISBN:	9781303001772

Model-based Inference of Gene Regulatory Networks.
Wang, Yuanfeng.

Model-based Inference of Gene Regulatory Networks. - 154 p.

Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2013.

This item must not be sold to any third party vendors.

In this thesis, I will present Markov model-based methods for inference and learning of gene regulatory systems. We studied different aspects of gene regulatory network. For small scale gene regulatory systems, we proposed a parameter inference method in systems modeled as Markov jump processes. Our method, based on maximum likelihood, uses stochastic gradient descent and reversible jump Markov chain monte-carlo to find the optimal parameter values. At the larger scale of gene regulatory networks, we uses a latent variable Gaussian graphical model to infer the network structure. We developed an efficient convex optimization algorithm based on Split-Bregman method for learning the inverse-covariance matrix and compared the model with the sparse Gaussian graphical model. Lastly I study epigenetic regulation and propose a Bayesian nework model that can learn biologically meaningful chromatin states and regulatory elements of the genome de novo from chromatin modifications.

ISBN: 9781303001772Subjects--Topical Terms:

518360
Biophysics.

Model-based Inference of Gene Regulatory Networks.
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100 1 $a Wang, Yuanfeng. $3 3175229
245 1 0 $a Model-based Inference of Gene Regulatory Networks.
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
500 $a Advisers: Thorsten Ritz; Xiaohui Xie.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a In this thesis, I will present Markov model-based methods for inference and learning of gene regulatory systems. We studied different aspects of gene regulatory network. For small scale gene regulatory systems, we proposed a parameter inference method in systems modeled as Markov jump processes. Our method, based on maximum likelihood, uses stochastic gradient descent and reversible jump Markov chain monte-carlo to find the optimal parameter values. At the larger scale of gene regulatory networks, we uses a latent variable Gaussian graphical model to infer the network structure. We developed an efficient convex optimization algorithm based on Split-Bregman method for learning the inverse-covariance matrix and compared the model with the sparse Gaussian graphical model. Lastly I study epigenetic regulation and propose a Bayesian nework model that can learn biologically meaningful chromatin states and regulatory elements of the genome de novo from chromatin modifications.
590 $a School code: 0030.
650 4 $a Biophysics. $3 518360
650 4 $a Bioinformatics. $3 553671
690 $a 0786
690 $a 0715
710 2 $a University of California, Irvine. $b Physics - Ph.D.. $3 2096691
773 0 $t Dissertation Abstracts International $g 74-07B(E).
790 $a 0030
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3557247