在本文中，我们提出了一个战略，结合各种特征选择方法来预测蛋白质的亚细胞位置。
In this paper, we propose a strategy to predict the subcellular locations of proteins by combining various feature selection methods.
最近邻算法是用来作为预测模型预测蛋白质的亚细胞位置，并获得一个正确的预测准确率70.63％，刀切交叉验证评估。
Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation.
蛋白质处于特定的亚细胞位置上才能行使其功能，故研究亚细胞定位对了解蛋白质功能非常重要。
Protein can work only in specific subcellular position, so where the protein located in a cell is very important for the study of the protein functions.
其次，我们探索了亚细胞位置之间的依赖关系，并且将这种关系用于支持向量机来进行蛋白质亚细胞定位。
Second, we explore the interdependences between subcellular locations and incorporates them with SVMs for prediction of protein subcellular localization.
其次，我们探索了亚细胞位置之间的依赖关系，并且将这种关系用于支持向量机来进行蛋白质亚细胞定位。
Second, we explore the interdependences between subcellular locations and incorporates them with SVMs for prediction of protein subcellular localization.
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