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Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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

Cited 11 times inthomson ciCited 13 times inthomson ci
Title
An efficient top-down search algorithm for learning Boolean networks of gene expression
Author
Nam, DouguSeo, SeunghyunKim, Sangsoo
Keywords
Boolean network; Core search; Coupon collection problem; Data consistency; Random superset selection
Issue Date
200610
Publisher
SPRINGER
Citation
MACHINE LEARNING, v.65, no.1, pp.229 - 245
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-1)). We show the efficiency of our algorithm via computational experiments, and present optimal parameters. Additionally, we provide tests for yeast expression data.
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DOI
http://dx.doi.org/10.1007/s10994-006-9014-z
ISSN
0885-6125
Appears in Collections:
SLS_Journal Papers

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