暂态混沌神经网络在蛋白质关联图预测中应用研究  

Research on Application of Transiently Chaotic Neural Network in Prediction of Protein Contact Maps

作　　者：刘桂霞[1] 吕晓枫[2] 徐春艳[3] 周春光[1] 

机构地区：[1]吉林大学计算机科学与技术学院,吉林长春130012 [2]吉林大学计算中心,吉林长春130012 [3]北华大学计算机科学与技术学院,吉林吉林132021

出　　处：《小型微型计算机系统》2008年第7期1291-1295,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金重点项目(60433020)资助;国家自然科学基金项目(60175024,60773095)资助

摘　　要：蛋白质结构预测是生物信息学的一个主要研究方向,而蛋白质关联图预测是其中的一个重要内容.针对蛋白质关联图预测问题,提出一种暂态混沌神经网络实现方法,所提出的方法具有暂态混沌特性和平稳收敛特性,能有效避免传统Hopfield神经网络极易陷入局部极值的缺陷.它通过短暂的倒分叉过程,能很快进入稳定收敛状态.仿真结果表明:暂态混沌神经网络解决蛋白质关联图预测问题时,总能收敛到全局最优或几乎接近全局最优,同时具有更高的搜索效率.这种方法预测精度达到0.27,比随机预测器高9倍.The protein structure prediction is a main direction in bioinformaties, and the prediction of protein contact maps is an important content in protein structure prediction. A algorithm based on transiently chaotic neural network is proposed to solve the protein contact maps problem. The proposed neural networks have many merits which are transient chaos and stable con- vergence etc. so as to overcome the drawbacks of easily getting stuck in local minim in conventional Hopfield neural networks. It can reach a stable convergent state after shortly reversed bifurcations. Simulation of protein contact maps problem show the transiently chaotic neural network has higher ability to search for global optimal or near-optimal solution and higher efficiency of searching than Hopfield neural networks. The method could assign protein contacts wkh an average accuracy of 0. 27 and with an improvement over a random predictor of a factor greater than 9.

关 键 词：蛋白质关联图 人工神经网络 暂态混沌神经网络 疏水性 二级结构 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]