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Model-based Inference of Gene Regula...

Wang, Yuanfeng.

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Model-based Inference of Gene Regulatory Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model-based Inference of Gene Regulatory Networks./
作者:	Wang, Yuanfeng.
面頁冊數:	154 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
Contained By:	Dissertation Abstracts International74-07B(E).
標題:	Biophysics. -
電子資源:	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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502 $a Thesis (Ph.D.)--University of California, Irvine, 2013.
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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.
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