"Ideal Parent" structure learning for continuous variable networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Clustering gene expression data via mining ensembles of classification rules evolved using moses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computational methods for estimation of cell cycle phase distributions of yeast cells
EURASIP Journal on Bioinformatics and Systems Biology
On temporal logic constraint solving for analyzing numerical data time series
Theoretical Computer Science
MCMC Based Bayesian Inference for Modeling Gene Networks
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
On the analysis of numerical data time series in temporal logic
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
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
High-resolution modeling of cellular signaling networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning Genetic Regulatory Network Connectivity from Time Series Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
The factor graph network model for biological systems
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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Motivation: Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems. Results: We present fine-grained dynamical models of gene transcription and develop methods for reconstructing them from gene expression data within the framework of a generative probabilistic model. Unlike previous works, we employ quantitative transcription rates, and simultaneously estimate both the kinetic parameters that govern these rates, and the activity levels of unobserved regulators that control them. We apply our approach to expression datasets from yeast and show that we can learn the unknown regulator activity profiles, as well as the binding affinity parameters. We also introduce a novel structure learning algorithm, and demonstrate its power to accurately reconstruct the regulatory network from those datasets.