Introduction to algorithms
Neural Computation
Error-free and best-fit extensions of partially defined Boolean functions
Information and Computation
Algorithms for choosing differential gene expression experiments
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
On the use of MDL principle in gene expression prediction
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
BioCAD: an information fusion platform for bio-network inference and analysis
TMBIO '06 Proceedings of the 1st international workshop on Text mining in bioinformatics
Inference of a probabilistic Boolean network from a single observed temporal sequence
EURASIP Journal on Bioinformatics and Systems Biology
Computational methods for estimation of cell cycle phase distributions of yeast cells
EURASIP Journal on Bioinformatics and Systems Biology
Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
Inference of gene regulatory networks based on a universal minimum description length
EURASIP Journal on Bioinformatics and Systems Biology
Polynomial models of gene dynamics
Neurocomputing
International Journal of Bioinformatics Research and Applications
Scalable approach for effective control of gene regulatory networks
Artificial Intelligence in Medicine
Improvement of computing times in boolean networks using chi-square tests
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Generating probabilistic Boolean networks from a prescribed stationary distribution
Information Sciences: an International Journal
Automated large-scale control of gene regulatory networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Automatica (Journal of IFAC)
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Asymptotical lower limits on required number of examples for learning boolean networks
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Learning genetic regulatory network connectivity from time series data
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Estimating gene networks from expression data and binding location data via boolean networks
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Reverse engineering of GRNs: an evolutionary approach based on the tsallis entropy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A novel strategy for plant breeding based on simulations of gene network models
International Journal of Bioinformatics Research and Applications
Inferring Boolean functions via higher-order correlations
Computational Statistics
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Boolean networks are a popular model class for capturing the interactions of genes and global dynamical behavior of genetic regulatory networks. Recently, a significant amount of attention has been focused on the inference or identification of the model structure from gene expression data. We consider the Consistency as well as Best-Fit Extension problems in the context of inferring the networks from data. The latter approach is especially useful in situations when gene expression measurements are noisy and may lead to inconsistent observations. We propose simple efficient algorithms that can be used to answer the Consistency Problem and find one or all consistent Boolean networks relative to the given examples. The same method is extended to learning gene regulatory networks under the Best-Fit Extension paradigm. We also introduce a simple and fast way of finding all Boolean networks having limited error size in the Best-Fit Extension Problem setting. We apply the inference methods to a real gene expression data set and present the results for a selected set of genes.