Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data
Pattern Recognition Letters - special issue on pattern recognition in practice V
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Towards a More Efficient Evolutionary Induction of Bayesian Networks
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networks
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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Reverse engineering of gene regulatory networks is a key issue for functional genomic. Indeed, unraveling complex interactions among genes is a crucial step in order to understand their role in cellular processes. High-throughput technologies such as DNA microarrays or ChIP on chip have in principle opened the door to network inference from data. However the size of available data is still limited compared to their dimension. Machine learning methods have thus to be worked out in order to respond to this challenge. In this work we focused our attention on modeling gene regulatory networks with Bayesian networks. Bayesian networks offer a probabilistic framework for the reconstruction of biological interactions networks using data, but the structure learning problem is still a bottleneck. In this paper, we use evolutionary algorithms to stochastically evolve a set of candidate Bayesian networks structures and find the model that best explains the small number of available observational data. We propose different kinds of recombination strategies and an appropriate technique of niching that ensure diversity among candidate solutions. Tests are carried out on simulated data drawn from a biorealistic network. The effect of deterministic crowding, a niching method, is compared to mutation for different kinds of recombination strategies and is shown to improve significantly the performances. Enhanced by deterministic crowding, our evolutionary approach outperforms K2, Greedy-search and MCMC, for training sets whose size is small compared to the standard in machine learning.