Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

BMC Bioinformatics

Research Article

Yong Li¹  Xi Bai¹  Yanming Zhu¹  Hua Cai¹  Lili Liu¹  Wei Ji¹  Dianjing Guo² 

[1] Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China;State Key Lab of Agrobiotechnology and Department of Biology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;

关键词: Gene Regulatory Network;    Discretization Method;    Regulatory Relation;    Total Accuracy;    Gene Regulatory Network Inference;   

DOI  :  10.1186/1471-2105-11-520

 received in 2010-06-27, accepted in 2010-10-19,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMicroarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data.ResultsIn this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies.ConclusionsOur results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.

【 授权许可】

Unknown   
© Li et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.