广西师范大学学报（自然科学版） ›› 2011, Vol. 29 ›› Issue (3): 136-141.

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蛋白质功能预测的蚁群优化算法

吴超, 钟一文   

  1. 福建农林大学计算机与信息学院,福建福州350002
  • 收稿日期:2011-05-10 出版日期:2011-08-20 发布日期:2018-12-03
  • 通讯作者: 钟一文(1968—),男(畲族),福建上杭人,福建农林大学教授,博士。E-mail:yiwenzhong@163.com
  • 基金资助:
    国家自然科学基金资助项目(30800713/C140102);福建省自然科学基金资助项目(2008J0316)

Protein Function Prediction Using Ant Colony Optimization Algorithm

WU Chao, ZHONG Yi-wen   

  1. College of Computer and Information Science,Fujian Agricultural and Forestry University,Fuzhou Fujian 350002,China
  • Received:2011-05-10 Online:2011-08-20 Published:2018-12-03

摘要： 预测蛋白质功能是后基因组时代最具挑战性的问题之一,在大规模数据下采用高性能的功能预测算法能够节省大量的实验时间和成本。利用基于蛋白质相互作用网络的全局优化模型,提出了蛋白质功能预测的蚁群优化算法,算法在考虑全局模型的同时还利用了网络的先验信息,提高了搜索效率,仿真结果表明,蚁群优化算法能够有效对蛋白质功能进行预测,并且对蛋白质相互作用网络中的假阳性、假阴性数据具有较高的容错能力。

关键词: 蛋白质相互作用网络, 功能预测, 蚁群优化算法, 全局优化模型

Abstract: Protein function prediction is one of the most challenging problems in the post-genome era.For high-throughput data,it can save time and cost considerably by using prediction algorithms with high performance.Using a global optimization model based on protein-protein interaction networks,anant colony optimization algorithm for protein function prediction is proposed.The proposed algorithm can use the benefits of global optimization model and the priori knowledge in the network simultaneously and improves its search efficiency.The simulation results show that the ant colony optimization has good performance on protein function prediction,and good fault-tolerant for false positive and false negative data in the protein-protein interaction network.

Key words: protein-protein interaction network, function prediction, ant colony optimization, global optimization model

中图分类号: 

  • Q811
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