Abstract:
The Grey Pseudo Amino Acid Component(Grey-PseAAC) is the combination of the protein amino acid component and the pseudo amino acid component which is derived from GM(2,1). The server is a predictor of protein subcellular location based on the Grey-PseAAC and the augmented covariant discriminant algorithm. The overall success rate is 77.6% by independent dataset applicationsof test. In contrast with the other predictors in the same dataset, the “Grey PortLoc predictor” is more effectively. The study develops applications of the grey theory in bioinformatics.
Key words:
subcellular location,
grey model GM(2,1),
Grey Pseudo Amino Acid Component(Grey-PseAAC),
subcellular location prediction server
摘要： 对于蛋白质氨基酸序列，使用GM(2,1)模型的参数作为伪氨基酸成分，加上各氨基酸在序列中所占比例，构成蛋白质的灰色伪氨基酸成分表示。利用扩大协方差算法预测亚细胞定位，开发基于该方法的亚细胞定位预测服务器。在相同的数据集上，对比实验结果显示，该预测服务器在总体预测率上达到77.6%，比其他预测方法优越。相关的研究拓展了灰色理论在生物信息学上的应用。
关键词:
亚细胞定位,
灰色模型GM(2,1),
灰色伪氨基酸成分,
亚细胞定位预测服务器
CLC Number:
LIN Wei-zhong; XIAO Xuan. Subcellular Location Prediction Based on GM(2,1)[J]. Computer Engineering, 2009, 35(8): 225-226.
林卫中;肖 绚. 基于GM(2,1)的亚细胞定位预测[J]. 计算机工程, 2009, 35(8): 225-226.