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N. Friedman (2004)
Inferring Cellular Networks Using Probabilistic Graphical ModelsScience, 303
Jiayin Wang, Yufei Huang, Maribel Sanchez, Yufeng Wang, Jianqiu Zhang (2006)
Reverse Engineering Yeast Gene Regulatory Networks using Graphical Models2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2
I. Pournara, L. Wernisch (2004)
Reconstruction of gene networks using Bayesian learning and manipulation experimentsBioinformatics, 20 17
C. Rangel, J. Angus, Zoubin Ghahramani, M. Lioumi, E. Sotheran, Alessia Gaiba, D. Wild, F. Falciani (2004)
Modeling T-cell activation using gene expression profiling and state-space modelsBioinformatics, 20 9
D. Husmeier (2003)
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networksBioinformatics, 19 17
N. Friedman, M. Linial, I. Nachman, D. Pe’er (2000)
Using Bayesian networks to analyze expression data
P. Brazhnik, A. Fuente, P. Mendes (2002)
Gene networks: how to put the function in genomics.Trends in biotechnology, 20 11
P. Spellman, G. Sherlock, Michael Zhang, V. Iyer, Klaus Anders, M. Eisen, P. Brown, D. Botstein, B. Futcher (1998)
Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.Molecular biology of the cell, 9 12
T. Cover, Joy Thomas (2005)
Elements of Information Theory
Wim Hordijk, Olivier Gascuel (2005)
Improving the efficiency of SPR moves in phylogenetic tree search methods based on maximum likelihoodBioinformatics, 21 24
H. Kitano (2002)
Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biologyCurrent Genetics, 41
Raymond Cho, M. Campbell, E. Winzeler, L. Steinmetz, Andrew Conway, L. Wodicka, T. Wolfsberg, A. Gabrielian, D. Landsman, D. Lockhart, Ronald Davis (1998)
A genome-wide transcriptional analysis of the mitotic cell cycle.Molecular cell, 2 1
Matthew Beal (2003)
Variational algorithms for approximate Bayesian inference
E. Segal, M. Shapira, A. Regev, D. Pe’er, D. Botstein, D. Koller, N. Friedman (2003)
Module networks: identifying regulatory modules and their condition-specific regulators from gene expression dataNature Genetics, 34
Edward George, R. McCulloch (1993)
Variable selection via Gibbs samplingJournal of the American Statistical Association, 88
SunYong Kim, S. Imoto, S. Miyano (2003)
Inferring gene networks from time series microarray data using dynamic Bayesian networksBriefings in bioinformatics, 4 3
We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data integration, an extremely important task in gene network research. We propose a variable selection formulation of gene regulation and develop an inference solution based on a variational Bayesian expectation maximization (VBEM) learning rule. This solution has better performance and lower complexity than the popular Monte Carlo sampling techniques. In addition, we develop a method to incorporate the often needed constraints into the VBEM algorithm, making it much more suitable for common cases of small data size. To further illustrate the advantage of the VBEM algorithm, we demonstrate a Bayesian data integration scheme using the soft information obtained from the VBEM algorithm. The efficacy of the proposed VBEM algorithm and the corresponding Bayesian data integration scheme is evaluated on both simulated data and the yeast cell cycle microarray data sets.
Artificial Life – MIT Press
Published: Jan 1, 2008
Keywords: Gene networks; microarray data; Bayesian inference; variational Bayesian expectation maximization; Bayesian data integration
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