Boolean networks provide a simple and intuitive model for gene regulatory networks, but a critical defect is the time required to learn the networks. In recent years, efficient network search algorithms have been developed for a noise-free case and for a limited function class. In general, the conventional algorithm has the high time complexity of O(22kmn k+1) where m is the number of measurements, n is the number of nodes (genes), and k is the number of input parents. Here, we suggest a simple and new approach to Boolean networks, and provide a randomized network search algorithm with average time complexity O (mn k+1/ (log m)(k-1)). We show the efficiency of our algorithm via computational experiments, and present optimal parameters. Additionally, we provide tests for yeast expression data. © Springer Science + Business Media, LLC 2006.
CITATION STYLE
Nam, D., Seo, S., & Kim, S. (2006). An efficient top-down search algorithm for learning Boolean networks of gene expression. Machine Learning, 65(1), 229–245. https://doi.org/10.1007/s10994-006-9014-z
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