%0 Journal Article %A GUO Lei %A WANG Shunfang %T Prediction of Membrane Protein Based on Sequence Information Fusion and Two-Stage Feature Selection %D 2019 %R 10.3778/j.issn.1002-8331.1712-0265 %J Computer Engineering and Applications %P 145-150 %V 55 %N 6 %X 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1712-0265