Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave, Tehran, Iran.
Comput Biol Med. 2013 Sep;43(9):1182-91. doi: 10.1016/j.compbiomed.2013.05.017. Epub 2013 Jun 3.
In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle.
本文提出了一种智能超框架，用于从氨基酸序列中识别蛋白质折叠，这是生物信息学中的一个基本问题。该框架包括一些用于蛋白质分类的统计和智能算法。所提出框架的主要组件是模糊资源分配网络（FRAN）和基于粒子群优化的径向基函数（RBF-PSO）。FRAN 采用动态方法调整 RBF 网络参数。由于蛋白质数据集捕获的模式复杂性，FRAN 在模糊条件下对蛋白质进行分类。此外，RBF-PSO 应用 PSO 调整 RBF 分类器。实验结果表明，FRAN 可将预测精度提高高达 51%，并可实现可接受的蛋白质折叠预测的多类结果。虽然 RBF-PSO 可为蛋白质折叠识别提供高达 48%的合理结果，但在某些情况下，它比 FRAN 弱。然而，该超框架为使用各种智能方法提供了机会，并可以从以往的经验中学习。因此，它可以避免一些智能方法在内存、计算时间和静态结构方面的弱点。此外，该系统的性能可以在系统的整个生命周期中得到增强。
