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- Title
An efficient top-down search algorithm for learning Boolean networks of gene expression.
- Authors
Dougu Nam; Seunghyun Seo; Sangsoo Kim
- Abstract
<span class="AbstractHeading">Abstract </span>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.
- Publication
Machine Learning, 2006, Vol 65, Issue 1, p229
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-006-9014-z