Autoregressive-model based dynamic fuzzy clustering for time-course gene expression data

被引：0

作者：

Xu, Hong-Lin ^{[1
]}

Liu, Yu-Hong ^{[1
]}

Wang, Shi-Tong ^{[1
]}

机构：

[1] School of Information, Southern Yantze University, Wuxi

来源：

Biotechnology | 2008年 / 7卷 / 01期

关键词：

Autoregressive model; Dynamic fuzzy clustering; Fuzzy clustering; Self-relationship; Time-course gene expression;

D O I：

10.3923/biotech.2008.59.65

中图分类号：

学科分类号：

摘要：

In this study, a novel algorithm called Dynamic Fuzzy Clustering (DFC) is proposed for clustering time-course gene expression data. The proposed method combines Autoregressive (AR) model and conventional Fuzzy Clustering Algorithm (FCM). Under this approach, a time-course gene expression data can be analyzed as a set of dynamic time series with AR model in order to utilize the important dynamic information more efficiently and the forecast process in AR model can be adjusted using the corresponding fuzzy membership such that better clustering results can be obtained. Experiments performed on a synthetic and two real-world time-course gene expression datasets also indicates that this proposed approach can be more effective than some other conventional clustering algorithms such as FCM and simple dynamic model-based clustering algorithm. © 2008 Asian Network for Scientific Information.

引用

页码：59 / 65

页数：6

共 14 条

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