Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of gene regulatory networks based on a universal minimum description length
EURASIP Journal on Bioinformatics and Systems Biology
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
A mathematical program to refine gene regulatory networks
Discrete Applied Mathematics
Spectral preprocessing for clustering time-series gene expressions
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Transcriptional gene regulatory network reconstruction through cross platform gene network fusion
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Selection of statistical thresholds in graphical models
EURASIP Journal on Bioinformatics and Systems Biology
Effects of cDNA microarray time-series data size on gene regulatory network inference accuracy
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pattern recognition in biological time series
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A scalable approach for inferring transcriptional regulation in the yeast cell cycle
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Application of logic synthesis to the understanding and cure of genetic diseases
Proceedings of the 49th Annual Design Automation Conference
Non-stationary bayesian networks based on perfect simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
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Motivation: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations. Results: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development. Availability: Available from the authors upon request. Contact: wtzhao@ece.tamu.edu