The Information Flow Clustering Model and Algorithm Based on the Artificial Bee Colony Mechanism of PPI Network
The clustering algorithm of Protein-Protein Interaction (PPI) networks is an important method to fully understand the organizations and functions of molecules and identify the functional modules of protein. There are lots of traditional clustering algorithms which do not perform well in clustering PPI networks. Recently functional flow simulation algorithm is a novel clustering algorithm. However, it does not take the effect of distance into account and the merging threshold is set manually which is subjective. This paper proposes a novel information flow clustering model and algorithm based on the mechanism of Artificial Bee Colony (ABC) optimization. This method firstly sorts the network comprehensive feature value of nodes to initialize the cluster centers during the procedure of data pre-processing. The nectar source of ABC algorithm is corresponding to cluster center, the income level of nectar stands for the similarity between modules. Afterwards all the adjacent nodes of employed bee node are sorted in the descending order according to the network comprehensive feature value of nodes, which are regarded as the searching neighborhood of scouts. In the end, the algorithm adopts precision, recall and other criteria to evaluate the cluster effect in an objective way. In addition, some significant parameters of the algorithm is simulated, compared and analyzed. The experiment results show that the new algorithm not only overcomes the shortcomings of original algorithm, but also the harmonic mean value of precision and recall gets greatly improved, which can effectively identify the functional modules of protein.
information flowArtificial Bee Colony (ABC) algorithmclusteringProtein-Protein Interaction (PPI) network
NETL
NSTL
万方数据
维普
