Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization

Lin, K. and Husmeier, D. (2009) Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization. EURASIP Journal on Bioinformatics and Systems Biology, 2009(601068),

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Abstract

Understanding the mechanisms of gene transcriptional regulation through analysis of high-throughput postgenomic data is one of the central problems of computational systems biology. Various approaches have been proposed, but most of them fail to address at least one of the following objectives: (1) allow for the fact that transcription factors are potentially subject to posttranscriptional regulation; (2) allow for the fact that transcription factors cooperate as a functional complex in regulating gene expression, and (3) provide a model and a learning algorithm with manageable computational complexity. The objective of the present study is to propose and test a method that addresses these three issues. The model we employ is a mixture of factor analyzers, in which the latent variables correspond to different transcription factors, grouped into complexes or modules. We pursue inference in a Bayesian framework, using the Variational Bayesian Expectation Maximization (VBEM) algorithm for approximate inference of the posterior distributions of the model parameters, and estimation of a lower bound on the marginal likelihood for model selection. We have evaluated the performance of the proposed method on three criteria: activity profile reconstruction, gene clustering, and network inference.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Lin, K., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:EURASIP Journal on Bioinformatics and Systems Biology
Publisher:Hindawi Publishing Corporation
ISSN:1687-4145
Published Online:12 April 2009
Copyright Holders:Copyright © 2009 The Authors
First Published:First published in EURASIP Journal on Bioinformatics and Systems Biology 2009:601068
Publisher Policy:Reproduced under a Creative Commons License

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