A novel method for predicting protein subcellular localization based on pseudo amino acid composition

被引：0
|
作者：
Ma, Junwei [1 ]
Gu, Hong [1 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
来源：
BMB REPORTS | 2010年 / 43卷 / 10期
关键词：
Elman neural network; Five-fold cross validation; Principal component analysis; Protein subcellular localization; Pseudo amino acid composition; ENSEMBLE CLASSIFIER; WEB-SERVER; LOCATION; RECOGNITION; NETWORKS; PLOC;
D O I：
10.3858/BMBRep.2010.43.10.670
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
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. [BMB reports 2010; 43(10): 670-6761
引用
收藏
页码：670 / 676
页数：7
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