基于 Convolutional-LSTM 的蛋白质亚细胞定位研究

The prediction study of protein subcellular location is one of the key issues in proteomics and bioinformatics research. Subcellular localization of proteins determines its biological function. Therefore, studying subcellular location is very important for understanding the protein function. Because of the sequential protein structure, this paper uses sequence model to carry out subcellular location research. This paper uses two models, convolutional neural network (CNN) and long short-term memory (LSTM) networks, to mine the information contained in the amino acid sequence so as to predict the subcellular location, followed by the integrated model of Convolutional-LSTM to locate subcellular. First, this paper uses convolutional neural network to extract features of protein sequence data. And then the features are combined and sent to the long short-term memory networks for studying characteristic. After that, the subcellular localization results are obtained. The accuracy of the model classification is 0.8165, which is significantly higher than traditional methods.