PPI (Point-Point Interaction) network clustering method based on artificial swarm reproduction mechanism

Abstract

The invention discloses a PPI (Point-Point Interaction) network clustering method based on an artificial swarm reproduction mechanism, comprising specific steps of: converting a PPI network into an undirected weighted graph; setting parameters; pre-treating each knot and each edge of the PPI network; calculating a weighted network comprehensive characteristic value of all the knots; initializing queen bees; carrying out a mating flight process; partially searching young bees; optimally selecting the queen bees; and selecting the current fitness and comparing until a global optimum clustering result is output. According to the method disclosed by the invention, the clustering quantity does not be pre-set and can be automatically obtained in a clustering process, so that the subjectivity of artificially setting the clustering quantity is avoided, and the time complexity is obviously reduced. An MIPS (Million Instructions Per Second) database is used for carrying out experiment simulation, a result is closer to a standard database, and indexes including the accuracy, the recall ratio, the operation time and the like are better. Compared with the other clustering methods, the method can automatically determine the clustering quantity by adopting the artificial swarm reproduction mechanism based on the reproduction mechanism, so that the clustering process is realized, and the clustering effect and the calculation efficiency are effectively improved.

Description

Technical field [0001] The present invention belongs to the field of biological information computing, and specifically relates to a clustering method for automatically obtaining the number of clusters in a protein-protein interaction (PPI) network. The PPI network has the characteristics of small world and scale-free, and the present invention can be generalized and applied For other small-world, scale-free network clustering problems. Background technique [0002] There are many existing clustering methods, mainly including: partition-based methods, density-based methods, network-based methods, model-based methods, hierarchical methods, fuzzy clustering methods, spectral clustering methods, functional flow Simulation methods, overall clustering methods, etc. However, these methods either have special requirements for the application field and data characteristics, or the methods themselves have some defects, some are not suitable for protein interaction (PPI) networks, and som...

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Problems solved by technology

[0013] Aiming at the defects or deficiencies of the above-mentioned existing clustering methods, the technical problem to be solved by the present invention is to overcome the subjectivity of artificially setting the number of clusters in advance when clustering problems of small-world and scale-free network function modules. Mechanism The mechanism of the artificial bee colony method can automatically determine the number of clusters, realize the clustering process, and effectively improve the clustering effect and calculation efficiency

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