关键词: 词袋模型, 粒子群算法, 细菌觅食, 亚细胞定位预测

Abstract: A bag of words model based on PSO_BFA optimization is proposed. The traditional bag of words model has two important parameters, the window size [d] and the dictionary size [k], respectively. By combining particle swarm optimization and bacterial foraging algorithm, a new integrated optimization algorithm called PSO_BFA is proposed. During the process of local search in PSO, the replication and migration behavior of BFA are added to obtain the best solution of the new PSO_BFA, which is the best combination of window size and dictionary size.Then the optimized BOW model combined with amino acid composition and pseudo amino acid composition is applied to extract the feature vectors of the protein sequences. The experimental results show that the BOW model optimized by PSO_BFA can effectively improve the accuracy of protein subcellular location prediction.

Key words: bag of words model, particle swarm optimization, bacterial foraging, subcellular localization prediction