基于伪氨基酸组成的蛋白质亚细胞定位预测新方法。
A novel method for predicting protein subcellular localization based on pseudo amino acid composition.
机构信息
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
出版信息
BMB Rep. 2010 Oct;43(10):670-6. doi: 10.5483/BMBRep.2010.43.10.670.
In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.
本文首次提出了一种新方法 ELM-PCA，用于预测蛋白质亚细胞定位。首先，通过伪氨基酸组成（PseAAC）来表示蛋白质样本。其次，采用主成分分析（PCA）提取必要特征。最后，使用 Elman 递归神经网络（RNN）作为分类器来识别蛋白质序列。结果表明，所提出的方法是有效和实用的。
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