Regression trees for regulatory element identification
Bioinformatics
Predicting genetic regulatory response using classification
Bioinformatics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
SMEM Algorithm for Mixture Models
Neural Computation
A combined expression-interaction model for inferring the temporal activity of transcription factors
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Motif discovery through predictive modeling of gene regulation
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
An improved statistic for detecting over-represented gene ontology annotations in gene sets
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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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.