Machine Learning

, Volume 65, Issue 1, pp 229–245 | Cite as

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

  • Dougu NamEmail author
  • Seunghyun Seo
  • Sangsoo Kim


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 mnk+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.


Boolean network Data consistency Random superset selection Core search Coupon collection problem 


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.National Genome Information CenterKorea Research Institute of Bioscience and BiotechnologyDaejeonRep. of Korea
  2. 2.Department of MathematicsSeoul National UniversitySeoulRep. of Korea
  3. 3.Department of BioinformaticsSoongsil Univ.SeoulRep. of Korea

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