Department of Mathematics, Shanghai University, Shanghai 200444, China.
Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
Biomed Res Int. 2014;2014:362738. doi: 10.1155/2014/362738. Epub 2014 Mar 6.
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.
在过去几十年中，随着基因组学、蛋白质组学、代谢组学和其他类型的组学技术的快速发展，产生了大量与分子生物学相关的数据。对于生物信息学家来说，使用传统的智能技术（例如支持向量机）来分析和解释这些数据已成为一项巨大的挑战。最近，由于其鲁棒性和效率，集成了几种标准智能方法的混合智能方法变得越来越流行。具体而言，基于进化算法 (EA) 的混合智能方法由于 EA 的效率和鲁棒性而在各个领域得到了广泛应用。在本文中，我们介绍了混合智能方法（特别是基于进化算法的混合智能方法）在生物信息学中的应用。特别地，我们重点介绍了它们在生物信息学中三个常见问题（即特征选择、参数估计和生物网络重建）中的应用。
