计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (33): 211-214.DOI: 10.3778/j.issn.1002-8331.2008.33.064

• 工程与应用 • 上一篇    下一篇

基于惯性法则的基因调控网络推断

应文豪1,2,王士同1

  

  1. 1.江南大学 信息工程学院,江苏 无锡 214122
    2.常熟理工学院 计算机科学与工程系,江苏 常熟 215500
  • 收稿日期:2007-12-11 修回日期:2008-03-14 出版日期:2008-11-21 发布日期:2008-11-21
  • 通讯作者: 应文豪

Gene regulatory networks inference based on inertia principle

YING Wen-hao1,2,WANG Shi-tong1   

  1. 1.School of Information Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
    2.School of Computer Sience and Engineering,Changshu Institute of Technology,Changshu,Jiangsu 215500,China
  • Received:2007-12-11 Revised:2008-03-14 Online:2008-11-21 Published:2008-11-21
  • Contact: YING Wen-hao

摘要: 结合基因调控网络本身的非线性特征,提出了一个改进型的基于惯性法则的微分动力学模型,并证明其具有递归神经网络特征。使用DNA修复网络的一组时序基因表达数据进行仿真实验,实验中用粒子群优化算法优化网络参数,得到了较有意义的结果。

关键词: 惯性法则, 基因调控网络, 粒子群优化算法, DNA修复网络, 时序基因表达数据

Abstract: By combining with genetic regulatory networks’ nonlinear characteristic,an improved differential dynamical model based on the inertia principle is presented in this paper.The derivations demonstrate that it can work like the recurrent neural network.The experimental results for a time series gene expression data of DNA Repair networks indicate that with the help of the particle-swarm-optimization based training procedure for the parameter adjustment,the proposed model is effective.

Key words: inertia principle, gene regulatory networks, particle swarm optimization, DNA repair networks, time series gene expression data