关键词: 膜蛋白预测, 最大信息系数, 遗传算法, 特征选择, 特征融合, 极端随机树

Abstract: Researching on membrane protein type prediction is of great significance, because the type of membrane protein is exceedingly related with its function. In this study, a two-stage feature selection method is proposed（MIC-GA）, which is on the basis of Maximum Information Coefficient（MIC） and Genetic Algorithm（GA）, to address the problem of high-dimensional feature in the process of feature extraction for membrane protein. Three kinds of feature representations, PseAAC, DC and PSSM, are extracted from a membrane protein sequence. In the process of feature fusion, an improved ReliefF algorithm（FReliefF） is proposed to obtain an effective feature score. Ultimately the extremely randomized tree is used two times based on Stacking ensemble learning framework to realize a reasonable prediction of membrane protein types. The results show that the proposed method can improve the accuracy of membrane protein prediction efficiently.

Key words: membrane protein type prediction, maximum information coefficient, genetic algorithm, feature selection, feature fusion, extremely randomized tree