A model for reasoning about persistence and causation
Computational Intelligence
On the use of MDL principle in gene expression prediction
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
Generating probabilistic Boolean networks from a prescribed stationary distribution
Information Sciences: an International Journal
Selection of statistical thresholds in graphical models
EURASIP Journal on Bioinformatics and Systems Biology
SFFS-MR: a floating search strategy for GRNs inference
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements.