Aussie-Sino Studies

2017, (4) P139-P146

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Bicluster Analysis for Estimating Missing Values in Gene Expression Data

Zhu Xian 1 & Chen Linlin 1 & Ma Wei 2 & Zhu Jun 1

摘要(Abstract):

Gene expression data are a large-scale matrix produced by DNA microarray experiments and can be used to effectively extract biological information. Gene expression data often have missing values due to experimental conditions, and such values should be filled. The traditional method for filling missing data is based on a single feature of gene expression data and does not consider the correlation among data matrices. Considering that a low bicluster mean square value leads to high correlation of gene expression data, this paper proposes a new method for filling the bicluster data (Trim-SA). The method uses simulated annealing to produce a bicluster that meets the conditions. The bicluster is then effectively corrected through data correlation, and the missing data are filled with local least squares method. Analysis of four groups of real gene expression data shows the high filling accuracy of the developed Trim-SA method.

关键词(KeyWords): gene expression data, missing data filling, bicluster, simulated annealing

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基金项目(Foundation): This research was supported by Natural Science Foundation of the Colleges and Universities in Jiangsu Province (No. 15KJB520017, No. 16KJB520019, 17KJB520013).

作者(Author): Zhu Xian 1 & Chen Linlin 1 & Ma Wei 2 & Zhu Jun 1

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