An efficient top-down search algorithm for learning Boolean networks of gene expression

Authors:
Dougu Nam;Seunghyun Seo;Sangsoo Kim
Affiliations:
National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Rep. of Korea 305-333;Department of Mathematics, Seoul National University, Seoul, Rep. of Korea 151-747;Aff1 Aff3
Venue:
Machine Learning
Year:
2006

Citing 4
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Abstract

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(22k mn 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驴1)). We show the efficiency of our algorithm via computational experiments, and present optimal parameters. Additionally, we provide tests for yeast expression data.