Niu Bing, Jin Yu-Huan, Feng Kai-Yan, Lu Wen-Cong, Cai Yu-Dong, Li Guo-Zheng
School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
Mol Divers. 2008 Feb;12(1):41-5. doi: 10.1007/s11030-008-9073-0. Epub 2008 May 28.
In this paper, AdaBoost algorithm, a popular and effective prediction method, is applied to predict the subcellular locations of Prokaryotic and Eukaryotic Proteins-a dataset derived from SWISSPROT 33.0. Its prediction ability was evaluated by re-substitution test, Leave-One-Out Cross validation (LOOCV) and jackknife test. By comparing its results with some most popular predictors such as Discriminant Function, neural networks, and SVM, we demonstrated that the AdaBoost predictor outperformed these predictors. As a result, we arrive at the conclusion that AdaBoost algorithm could be employed as a robust method to predict subcellular location. An online web server for predicting subcellular location of prokaryotic and eukaryotic proteins is available at http://chemdata.shu.edu.cn/subcell/ .
在本文中，流行且有效的预测方法AdaBoost算法被应用于预测原核生物和真核生物蛋白质的亚细胞定位，该数据集源自SWISSPROT 33.0。通过重新代入检验、留一法交叉验证（LOOCV）和刀切法检验对其预测能力进行了评估。通过将其结果与一些最流行的预测器（如判别函数、神经网络和支持向量机）进行比较，我们证明了AdaBoost预测器优于这些预测器。因此，我们得出结论，AdaBoost算法可作为一种可靠的方法用于预测亚细胞定位。可通过http://chemdata.shu.edu.cn/subcell/访问用于预测原核生物和真核生物蛋白质亚细胞定位的在线网络服务器。
