CAJ | 학술논문

从 DNA序列片段个案中密码子分布密度角度出发，提取出DNA序列片段的特征，基于氨基酸分子中侧链基极性性质把氨基酸分成5大类，计算5大类出现的频率，这种考虑生物意义的特征提取方法不仅考虑碱基的含量，还在一定程度上考虑碱基的排列顺序，应用层次聚类分析方法和 BP神经网络法对DNA序列片段进行分类。结果表明，2类算法分类结果精度较高，且一致性也较高。说明这种特征提取法比传统的单纯考虑碱基的特征提取法效果更优。
종 DNA서렬편단개안중밀마자분포밀도각도출발，제취출DNA서렬편단적특정，기우안기산분자중측련기겁성성질파안기산분성5대류，계산5대류출현적빈솔，저충고필생물의의적특정제취방법불부고필감기적함량，환재일정정도상고필감기적배렬순서，응용층차취류분석방법화 BP신경망락법대DNA서렬편단진행분류。결과표명，2류산법분류결과정도교고，차일치성야교고。설명저충특정제취법비전통적단순고필감기적특정제취법효과경우。
The features of DNA sequence fragments were extracted from the distri-bution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five cate-gories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also con-sidered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence frag-ments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.