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

Citation data:

Machine Learning, ISSN: 0885-6125, Vol: 65, Issue: 1, Page: 229-245

Publication Year:
2006
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Citations 20
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Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/7185
DOI:
10.1007/s10994-006-9014-z
Author(s):
Nam, Dougu; Seo, Seunghyun; Kim, Sangsoo
Publisher(s):
Springer Nature; SPRINGER
Tags:
Engineering; Computer Science; Boolean network; Core search; Coupon collection problem; Data consistency; Random superset selection
article description
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(2) 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/ (log m)). 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.