北京生物醫(yī)學(xué)工程

基于次生特征提取方法預(yù)測(cè)蛋白質(zhì)同源寡聚體

Prediction of Protein HomoOligomer Types With a Novel Approach of Secondary Feature Extraction

作者：李啟鵬張紹武潘泉陳偉

單位：西北工業(yè)大學(xué)自動(dòng)化學(xué)院（西安710072）

關(guān)鍵詞：同源寡聚體；支持向量機(jī)；特征提取；原生特征；次生特征

分類號(hào)：

出版年·卷·期（頁碼）：2010·29·1（16-22）

摘要：

寡聚蛋白質(zhì)相對(duì)于單體蛋白質(zhì)具有許多優(yōu)勢(shì)，廣泛地參與多種生命活動(dòng)。本文提出次生特征提取方法, 使用支持向量機(jī)作為分類器, 采用“ 一對(duì)一”的多類分類策略, 基于蛋白質(zhì)一級(jí)序列提取特征方法，對(duì)四類同源寡聚體進(jìn)行分類研究。結(jié)果表明, 在Jackknife檢驗(yàn)下, 基于次生特征和氨基酸組成成分特征構(gòu)成的特征集, 加權(quán)情況下，其總分類精度最高達(dá)到了78.41%, 比氨基酸組成成分特征提高13.09%,比參考文獻(xiàn)最好特征集BG提高了6.86%，比最好原生特征集CM1提高了5.53%。此結(jié)果說明次生特征提取方法對(duì)于蛋白質(zhì)同源寡聚體分類是一種非常有效的特征提取方法。

Protein homooligomers play an important role in various life processes. The secondary feature extraction method was proposed and used for predicting protein homooligomers. Processing primary features by statistical methods to increase the distance among primary features, secondary feature can be obtained. The support vector machine （SVM） was used as base classifier. The 78.41% total accuracy was arrived in jackknife test in the weighted factor conditions, which was 13.09%，6.86% and 5.53% higher than those of conventional amino acid composition methods, that of the reference feature set BG and that of the best primary feature set CM1 in same condition respectively. The experimental results showed that the secondary feature extraction method is effective to increase the distance among primary features and improved the classification prediction performance.

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