关于DNA序列分类问题的模型

本文提出了一种将人工神经元网络用于 DNA分类的方法 .作者首先应用概率统计的方法对 2 0个已知类别的人工 DNA序列进行特征提取 ,形成 DNA序列的特征向量 ,并将之作为样本输入 BP神经网络进行学习 .作者应用了 MATLAB软件包中的 Neural Network Toolbox(神经网络工具箱 )中的反向传播 ( Backpropagation BP)算法来训练神经网络 .在本文中 ,作者构造了两个三层 BP神经网络 ,将提取的 DNA特征向量集作为样本分别输入这两个网络进行学习 .通过训练后 ,将 2 0个未分类的人工序列样本和 1 82个自然序列样本提取特征形成特征向量并输入两个网络进行分类 .结果表明 :本文中提出的分类方法能够以很高的正确率和精度对 DNA序列进行分类 ,将人工神经元网络用于 DNA序列分类是完全可行的

A Model for DNA Sequence Clustering Problem

This paper presents a method applying artificial neural network (NN) to DNA clustering problem. First we use the probability statistics method to extract the characters from the 20 artificial DNA sequences whose categories are known. Thus we can get the character vectors of the DNA sequences and input them as samples into BP neuron NN for learning. Weemploy the BP (back propagation) algorithm to train NN by use of the Neural Network Toolbox in MATLAB software package. In this paper, two three-story NN are created to input the extracted DNA character vectors as samples into them. After the training, characters are extracted from the 20 unclassified artificial sequence samples and 182 natural sequence samples to form the character vectors as input of the two NN for clustering. The results shows: the clustering method presented in this paper can classify the DNA sequences in quite high accuracy and precision. It is quite feasible to apply the artificial neural network to DNA sequence clustering.