Identifying nuclear protein subcellular localization using feature dimension reduction method

Tong Wang, Qinghua Huang, Lihua Hu
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Abstract

The subcellular location of a protein is closely correlated to its function. Facing the deluge of protein sequences generated in the post-genomic age, it is necessary to develop useful machine learning tools to identify the protein subcellular localization. DR (Dimensional Reduction) method is one of most famous machine learning tools. Some researchers have begun to explore DR method for computer vision problems such as face recognition, few such attempts have been made for classification of high-dimensional protein data sets. In this paper, DR method is employed to reduce the size of the features space. Comparison between linear DR methods (PCA and LDA) and nonlinear DR methods (KPCA and KLDA) is performed to predict subcellular localization of nuclear proteins. Experimental results thus obtained are quite encouraging, which indicate that the DR method is used effectively to deal with this complicated problem of viral proteins subcellular localization prediction. The overall jackknife success rate with KLDA is the highest relative to the other DR methods.
特征降维法识别核蛋白亚细胞定位
蛋白质的亚细胞位置与其功能密切相关。面对后基因组时代产生的大量蛋白质序列,有必要开发有用的机器学习工具来识别蛋白质的亚细胞定位。DR(降维)方法是最著名的机器学习工具之一。一些研究者已经开始探索DR方法在人脸识别等计算机视觉问题上的应用,但对于高维蛋白质数据集的分类研究还很少。本文采用DR方法减小特征空间的大小。比较了线性DR方法(PCA和LDA)和非线性DR方法(KPCA和KLDA)对核蛋白亚细胞定位的预测。实验结果令人鼓舞,表明DR方法可以有效地解决复杂的病毒蛋白亚细胞定位预测问题。与其他DR方法相比,KLDA的综合刀切成功率最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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