文章基本信息

标题：Integrative Estimation of Gene Reguratory Network by means of AdaBoost
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作者：Shinya Nabatame ; Hitoshi Iba
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2007
卷号：22
期号：5
页码：508-519
DOI：10.1527/tjsai.22.508
出版社：The Japanese Society for Artificial Intelligence
摘要：In order to estimate Gene Regulatory Networks (GRNs) from gene expression time series data, various recurrence or differential equation based models have been proposed, such as S-system, Linear model etc. Generally, it is assumed that a specific recurrence or differential equation model is sufficient to estimate the network from the expression profile. However, with so many different models available, it is not easy to recognize the model that will be most suitable for a particular network inference problem. To deal with the problem, integrative estimation with multiple recurrence or differential equation based models seems promising. In this paper, we propose the integration of multiple estimation methods by means of AdaBoost. Empirical studies show the effectiveness of our proposal.
关键词：gene regulatry network ; AdaBoost