SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Bio-logic: Gene expression and the laws of combinatorial logic
Artificial Life
Bioinformatics
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring gene interaction networks from ISH images via kernelized graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A pattern-oriented specification of gene network inference processes
Computers in Biology and Medicine
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Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.