Based on PPI Network and Deep Learning Predicte Protein Function Module Algorithm

The existing Protein protein interaction(PPI) based prediction algorithm for protein functional modules has the disadvantages of low accuracy and less number of functional modules. This paper presents an algorithm based on PPI network and in-depth learning and prediction of protein function module. In this method, three important attributes (node location, PPI network structure and core nodes) are integrated to improve the prediction accuracy and increase the number of protein functional modules. In this paper, First use the Local Density-Based Methods (LDBM) improved density clustering algorithm for clustering analysis. After clustering, use the improved to isometric mapping dimension reduction algorithm for principal component analysis (PPIPCA); then use Multi Layer Perceptron (MLP) for training and function module selection. Last Based on PPI network and deep learning based prediction protein function module algorithm (LPMM) compared with other algorithms. On the basis of yeast dip data set, the accuracy, F value, dimensionality reduction rate and functional module of recognition protein of LPMM algorithm and acc-fmd, MCL and mcode algorithm based on PPI network were compared. Experimental results show that LPMM algorithm is superior to other algorithmsin accuracy, F value, dimensionality reduction rate, number of recognition modules and so on.