KNN法在含纤连蛋白域蛋白质亚细胞定位中的应用  

Subcellular localization prediction of proteins containing fibronectin domains based on K-nearest neighbor algorithm

作　　者：李立奇[1] 张瑗[1] 周跃[1] 王开发[2] 

机构地区：[1]第三军医大学新桥医院,重庆400037 [2]第三军医大学计算机教研室

出　　处：《山东医药》2011年第2期20-21,共2页Shandong Medical Journal

基　　金：国家自然科学基金资助项目(30901512)

摘　　要：目的探讨K最近邻(KNN)法在含纤连蛋白(FN)域蛋白质亚细胞定位中的应用价值。方法选取含FN域蛋白质80个(40个细胞外蛋白质和40个细胞内蛋白质),采用KNN法进行蛋白质亚细胞定位,并采用jack-knife检验法和5维交叉验证法检验样本的定位的准确率。结果 KNN法定位细胞内蛋白36个,细胞外蛋白35个。jackknife法检验KNN法蛋白质定位准确率为88.75%,5维交叉法验证其定位准确率为82.5%。结论利用KNN法可较准确的预测含FN域蛋白质的亚细胞位置。Objective To evaluate the application value of a model based on K-nearest neighbor （KNN） method for subcellular localization prediction of proteins containing fibronectin domains. Methods KNN was used to predict the subcellular locations of proteins containing fibronectin domains. Amino acid compositions of 40 extraceUular proteins and 40 intracellular proteins were used as the input vectors which were divided into groups to be trained and tested. The prediction quality was examined by two standard test methods in statistics： the jackknife test and 5-fold cross validation. Results The prediction accuracy rate of the samples thus obtained via the jackknife test was 88.75%. 36 intracellular proteins and 35 extracellular proteins were predicted belonging to the right subcellular locations. While the 5-fold cross validation success rate was 82.5%. 34 intracellular proteins and 32 extracellular proteins were predicted belonging to the right locations. Conclusion The KNN method can provid good prediction performance for the protein sequences.

关 键 词：K最近邻法 纤连蛋白 亚细胞定位 氨基酸组成 

分 类 号：R604[医药卫生—外科学]