Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2009 Article ID 601068 26 pages doi 2009 601068 Research Article Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization Kuang Lin and Dirk Husmeier Biomathematics Statistics Scotland BioSS Edinburgh EH93JZ UK Correspondence should be addressed to Dirk Husmeier dirk@ Received 2 December 2008 Accepted 27 February 2009 Recommended by Debashis Ghosh 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. Copyright 2009 K. Lin and D. Husmeier. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and .
