Prediction of Bacteriophage Protein Locations Using Deep Neural Networks

被引:1
作者
Ali, Muhammad [1 ]
Taniza, Farzana Afrin [1 ]
Niloy, Arefeen Rahman [1 ]
Saha, Sanjay [1 ]
Shatabda, Swakkhar [1 ]
机构
[1] United Int Univ, Dept Comp Sci & Engn, Madani Ave, Dhaka, Bangladesh
来源
EMERGING TECHNOLOGIES IN DATA MINING AND INFORMATION SECURITY, IEMIS 2018, VOL 1 | 2019年 / 755卷
关键词
Supervised learning; Deep neural networks; Feature selection; Protein subcellular localization; IDENTIFICATION; EVOLUTIONARY; SELECTION;
D O I
10.1007/978-981-13-1951-8_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In phage therapy, bacteriophage proteins are used to kill bacteria that cause infection. The knowledge of the location of the bacteriophage proteins plays an important role here. In this paper, we propose a supervised learning based method to predict the locations of bacteriophage proteins. First, we address the problem of predicting whether a bacteriophage is extracellular or located in the host cell. Second, we also address the subcellular location prediction problem of the phage proteins. For the host located proteins, the proteins could either be located in cell membrane or in the cytoplasm. We have successfully used deep feed-forward neural network on a standard training dataset and achieved good results for both of the prediction problems. Our method uses an optimal set of features for classification and achieves 87.7% and 98.5% accuracy for two of the prediction problems which is 3.5% and 6.3% improved than the previous state-of-the-art results achieved for these problems, respectively.
引用
收藏
页码:29 / 38
页数:10
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