基于模糊蚁群的加权蛋白质复合物识别算法

iming at the problem that the accuracy and recall of the protein complexes identification algorithm based on ant colony and fuzzy C-means (FCM) clustering are not high and the running efficiency is low, this paper proposed a novel protein complex recognition algorithm named FAC-PC (algorithm for identifying weighted protein complexes based on fuzzy ant colony clustering) . Firstly, combing with the Pearson correlation coefficient and edge aggregation coefficient, it constructed the weighted protein network. Secondly, in order to overcome the defects of massive merger, filter, repeated pick-up and drop-down operations in ant colony clustering algorithm, it designed the EPS (essential protein selection) metric to select essential protein, and designed the PFC (protein fitness calculation) metric to traverse neighbors of essential proteins to obtain essential group proteins, then the essential group protein replaced the seed node in the process of ant colony clustering, which improved results that the accuracy and time performance. Furthermore, it proposed the SI (similarity improvement) metric to optimize the probability of picking and dropping operations of ant colony to obtain the number of clustering. Finally, according to the improved ant colony algorithm, it obtained the essential protein and the number of clustering to initialize the FCM algorithm, and designed the membership update strategy to optimize the membership update, at the same time, a new FCM objective function which took a balance between intra-clustering and proposed inter-clustering variation, finally identified the protein complex by improved FCM algorithm. It used FAC-PC algorithm to identify protein complexes on DIP data. The experimental results show that FAC-PC algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.