Artificial neural network model for predicting protein subcellular location

被引：24
作者：
Cai, YD
Liu, XJ
Chou, KC
机构：
[1] Chinese Acad Sci, Shanghai Res Ctr Biotechnol, Shanghai 200233, Peoples R China
[2] Univ Edinburgh, Inst Cell Anim & Populat Biol, Edinburgh EH9 3JT, Midlothian, Scotland
[3] Upjohn Labs, Comp Aided Drug Discovery, Kalamazoo, MI 49001 USA
来源：
COMPUTERS & CHEMISTRY | 2002年 / 26卷 / 02期
关键词：
protein subcellular location; neural network; T. Kohonen's self-organization model;
D O I：
10.1016/S0097-8485(01)00106-1
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
The function of a protein is closely correlated to its subcellular location. Is it possible to utilize a bioinformatics method to predict the protein subcellular location? To explore this problem. proteins are classified into 12 groups (Protein Eng. 12 (1999) 107-118) according to their subcellular location: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria. (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. In this paper, the neural network method was proposed to predict the subcellular location of a protein according to its amino acid composition. Results obtained through self-consistency. cross-validation and independent dataset tests are quite high. Accordingly, the present method can serve as a complement tool for the existing prediction methods in this area. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：179 / 182
页数：4
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