神经网络在基因序列预测中的应用研究

人类基因组计划的开展随之产生了巨量的基因组信息，区分DNA 序列上的外显子和内含子成为基因工程中对基因进行识别和鉴定关键环节之一[1]。如何建立良好的系统模型将基因组数据进行有效地存储、分析和挖掘，仍是难题。本文着重研究将多层前馈神经网络应用于基因序列的预测分析中，成功从基因序列上识别出剪接位点，进而区分内含子和外显子边界。使用MATLAB 神经网络工具箱和图形用户界面开发威廉希尔官方网站 ，对UCI机器学习数据库中的基因数据集采用二进制数字编码，完成样本选取；创建优化算法的BP神经网络和GRNN 神经网络并加以训练、仿真和测试。
关键词 神经网络；基因工程；内含子；外显子
Abstract With the development of HGP, lots of genome information is acquired. To divide
the exorns and introns in DNA sequence is one of the key points of genetic identification in
genetic engineering. How to make a system model to storage，analyze and digging the genome data is still very difficult. This paper concentrates on how to use multilayer feedforward NN to getting primate-junction from genome sequence, and then, get the boundaries between exorns and introns. By using neural network tool box of MATLAB and GUI technology, to encode the data set from UCI Machine Learning Database with binary digit code, to gain examples.The designed BPNN model and GRNN model are used to train, simulate and test the above examples, and then predict unknown gene classification data from the model.
Key word artificial neural networks; genetic engineering; exorns; introns