A comparison study on feature selection of DNA structural properties for promoter prediction

BMC Bioinformatics

Research Article

Yanglan Gan¹  Jihong Guan¹  Shuigeng Zhou² 

[1] Department of Computer Science and Technology, Tongji University, Shanghai, China;Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, Shanghai, China;

关键词: Feature Selection;    Feature Subset;    Feature Selection Method;    Feature Selection Technique;    Promoter Prediction;   

DOI  :  10.1186/1471-2105-13-4

 received in 2011-07-27, accepted in 2012-01-07,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundPromoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task.ResultsThis paper conducts an extensive comparison study on feature selection of DNA structural properties for promoter prediction. Firstly, to examine whether promoters possess some special structures, we carry out a systematical comparison among the profiles of thirteen structural features on promoter and non-promoter sequences. Secondly, we investigate the correlations between these structural features and promoter sequences. Thirdly, both filter and wrapper methods are utilized to select appropriate feature subsets from thirteen different kinds of structural features for promoter prediction, and the predictive power of the selected feature subsets is evaluated. Finally, we compare the prediction performance of the feature subsets selected in this paper with nine existing promoter prediction approaches.ConclusionsExperimental results show that the structural features are differentially correlated to promoters. Specifically, DNA-bending stiffness, DNA denaturation and energy-related features are highly correlated with promoters. The predictive power for promoter sequences differentiates greatly among different structural features. Selecting the relevant features can significantly improve the accuracy of promoter prediction.

【 授权许可】

Unknown   
© Gan et al; licensee BioMed Central Ltd. 2012. 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.