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

• Published in: Machine learning Vol. 65; no. 1; pp. 229 - 245
• Main Authors: Nam, Dougu, Seo, Seunghyun, Kim, Sangsoo
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.10.2006; Springer Nature B.V
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Studies; Coupon; Probabilistic approach; Top down method; Information retrieval; Coupon collection problem; Randomized algorithm; Gene expression; Modeling; Core search; Search algorithm; Boolean expression; Boolean network, Data consistency, Random superset selection; Randomized design; Boolean logic; Algorithm complexity; Defect; Genetics; Learning algorithm; Bioinformatics; Time complexity; Artificial intelligence
• ISSN: 0885-6125; 1573-0565; 1573-0565
• DOI: 10.1007/s10994-006-9014-z

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^sup 2k^ ^sup 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 ^sup (mn k+1^/ (log m)^sup (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 ABSTRACT]