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Title 

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

Authors 

Dougu NamS SeoS Kim

Publisher 

Springer Verlag (Germany)

Issue Date 

2006

Citation 

Machine Learning, vol. 65, no. 1, pp. 229-245

Keywords 

boolean networkcore searchcoupon collection problemdata consistencyrandom superset selectionneural networksonline searchingboolean networksrandom superset selections

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(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.

ISSN 

0885-6125

Link 

http://dx.doi.org/10.1007/s10994-006-9014-z

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2017-04-19


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