Mining Time Series Gene Expression Data for Gene regulatory Networks
Date Issued
2005
Date
2005
Author(s)
Chen, Yen-Fu
DOI
en-US
Abstract
Time series gene expression data can be exploited to reveal causal genetic events. However, current methods of gene network modeling focus on one sample of the dataset, which may suffer from a low recovery rate. Moreover, gene network modeling emphasizes small set of genes because of high computation time. Our proposed approach efficiently mines gene regulatory patterns from large scale of replicate time series datasets. The patterns can be used to generate gene regulatory networks. The regulatory networks reveal the relationships of dynamic causal regulatory events and their regulatory intensities.
We first examine our proposed approach with simulated data for performance evaluation. In addition, we also apply our proposed approach to human cell cycle data. The results show that our proposed method is not only efficient and scalable but reveals complex regulatory information among large scale of genes.
Subjects
基因表現資料分析
基因調控網絡
序列性資料探勘
gene expression data analysis
gene regulatory networks
sequential data mining
Type
other
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