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Review
. 2014:2014:362738.
doi: 10.1155/2014/362738. Epub 2014 Mar 6.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics

Shan Li  1 , Liying Kang  1 , Xing-Ming Zhao  2
Affiliations
Review

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics

Shan Li et al. Biomed Res Int. 2014.

Abstract

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

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Figures

Figure 1
Figure 1
The schematic flowchart of genetic algorithm.
Figure 2
Figure 2
The flowchart of feature selection based on GA and classifier.
Figure 3
Figure 3
The reconstruction of gene regulatory network based on gene expression with the hybrid method consisting of Boolean network and evolutionary algorithm.
See this image and copyright information in PMC

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