| 改进的时序基因表达数据动态聚类算法 |
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| 引用本文： | 刘宇宏,王士同,徐红林. 改进的时序基因表达数据动态聚类算法[J]. 计算机工程与应用, 2007, 43(27): 164-167 |
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| 作者姓名： | 刘宇宏 王士同 徐红林 |
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| 作者单位： | 江南大学,信息工程学院,江苏,无锡,214122;江南大学,信息工程学院,江苏,无锡,214122;江南大学,信息工程学院,江苏,无锡,214122 |
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| 摘 要： | 文[1]采用了一种基于动态模型的聚类算法,将时序基因表达数据作为一组时间序列进行动态的聚类分析,得到了较为理想的聚类结果。对上述算法在数据初始化方面进行了合理改进,并利用贝叶斯理论对数据的联合概率分布进行了重新分析。实验表明,提出的改进算法所得聚类结果明显优于原算法所得结果。
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| 关 键 词： | 时序基因表达数据 自回归模型 动态模型 贝叶斯理论 |
| 文章编号： | 1002-8331（2007）27-0164-04 |
| 修稿时间： | 2007-01-01 |
Improved dynamic model-based clustering for time-course gene expression data |
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| LIU Yu-hong,WANG Shi-tong,XU Hong-lin. Improved dynamic model-based clustering for time-course gene expression data[J]. Computer Engineering and Applications, 2007, 43(27): 164-167 |
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| Authors: | LIU Yu-hong WANG Shi-tong XU Hong-lin |
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| Affiliation: | School of Information Engineering,Southern Yangtze University,Wuxi,Jiangsu 214122,China |
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| Abstract: | This paper refers to a dynamic model-based clustering algorithm in ,which can analyze a time-course gene expres- sion data as a set of time series dynamlcally,such that better clustering results can be produced.Some reasonable improvements are used in the initialization hereinafter.And the joint probability distribution for the time-course gene expression dataset is also reanalyzed using Bayes theory.Experimented results demonstrate that the results obtained by the improved clustering algorithm are better than those obtained by the dynamic model-based clustering algorithm. |
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| Keywords: | tlme-course gene expression autoregressive equatlon dynamlc model Bayes theory |
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