Improved Convolutional Neural Networks for Identifying Subcellular Localization of Gram-Negative Bacterial Proteins

Abstract

Prediction of subcellular localization of Gram-negative bacterial proteins plays a vital role in the development of antibacterial drugs. Computational approaches have made remarkable progress in bacterial protein subcellular localization, but disadvantages still exist. Recently, deep learning has received significant attention in bioinformatics and one of the key steps in prediction of subcellular localization is developing a powerful predictor. Therefore, improved convolutional neural networks (ICNN) is used to improve the performance of multi-site prediction. First of all, Amphiphilic pseudo amino acid based features (Ampseaac) is used to extract features. Then, compared to the multi-label k-nearest neighbor algorithm (MLKNN), ICNN is developed to identify the subcellular localization of Gram-negative bacterial proteins. The best overall accuracy of Ampseaac from ICNN predictor is 65.25%, better than MLKNN predictor 58.58%.