Research On Protein Feature Extraction And Function Prediction Based On Fuzzy Clustering Of Grasshopper Group

The systematic study of protein interactions has rapidly become the most important field in the post-genome revolution era.In recent years,it has received more attention from scholars of various countries.Experimental methods have been unable to meet the increasing demand for functional annotation of proteins due to their slow and high cost of extracting useful information from protein sequences.Because of its advantages of low cost and easy realization,the computational method has become the mainstream method to predict protein function.However,some common calculation methods are susceptible to noise data and have low accuracy.This paper conducts research on the above issues,and the research contents are as follows:(1)A feature extraction method IFCM-GOA for protein functional modules combining grasshopper algorithm GOA with intuitionistic fuzzy c-means clustering of IFCM is proposed.The IFCM algorithm can well interpret the characteristics of proteins belonging to multiple functional modules.At the same time,grasshopper algorithm can effectively solve the defects of the IFCM algorithm that is sensitive to clustering centers to improve the accuracy of clustering results.Compared with FCM algorithm and IFCM algorithm,the results show that IFCM-GOA algorithm performs better than the other two algorithms.It is feasible to extract the feature of protein functional modules.(2)A protein function prediction method IGP-DNN based deep neural network is proposed.In this algorithm,the feature information of protein functional modules is combined with the protein attribute information after the reduction of dimension by the kernel principal component analysis as the protein functional characteristics.The experimental comparison is conducted on three data sets and the appropriate superparameters are selected to construct the deep neural network IGP-DNN to predict the unknown protein function.Comparing IGP-DNN algorithm with IGP-SVM algorithm,HPMM algorithm and FFPred algorithm,the results show that IGP-DNN algorithm can effectively predict the unknown protein function.In the DIP data set,the precision,recall rate and f-measure can reach 0.4903,0.4051 and 0.4436.The algorithm also shows significant advantages in Krogan and Gavin data sets.(3)A synthetic analysis platform for protein network clustering was designed and implemented.The platform realizes a variety of different protein clustering methods to extract functional modules.The effect of clustering algorithm is evaluated by differentevaluation methods and rich visual interface is provided.