A method of identifying protein compounds by using a fruit fly optimization method

Abstract

The invention provides a method of identifying protein compounds by using a fruit fly optimization method. The method comprises the steps of converting a protein-protein interaction network into a undirected graph, performing pretreatment on the edges and nodes of the protein-protein interaction network, establishing a dynamic protein-protein interaction network, setting parameters, forming fruit fly positions, matching fruit flies with the protein-protein interaction network, determining initialization fruit fly positions, determining the fruit fly odor concentration, updating the fruit fly positions, generating a protein compound, and filtering the protein compound. The method gives full consideration to the dynamic nature of the protein network, the protein compound inner core-attachment structure and the locality and wholeness of the protein-protein interaction network and can identify protein compounds accurately. The results of simulation experiments show that the performance of the indexes such as the accuracy and the recall ratio are excellent. Compared with other clustering methods, the method, based on the characteristics of the protein network and the protein compounds, realizes the protein compound identification process and improves the protein compound identification accuracy.

Description

technical field [0001] The invention belongs to the field of biological information, in particular to a method for identifying protein complexes in a dynamic protein interaction network. Background technique [0002] At present, with the birth of high-throughput technology, a large amount of protein interaction (protein interaction) data has been detected, and it is becoming more and more important to detect protein complexes by computer to understand the function of unknown proteins and predict diseases. The interaction between proteins changes differently as cells enter different life cycles, so constructing a network that can more realistically simulate the dynamic interactions between proteins before clustering plays an important role in the identification of protein complexes. important role. Most of the current methods for identifying protein complexes through computer clustering work on static protein interaction networks, ignoring the dynamic changes in the connecti...

AI Technical Summary

Problems solved by technology

But they generate protein complexes mainly through some heuristic rules without taking into account the network properties of the entire protein interaction

[0009] The shortcomings of the above clustering methods do not take into account the dynamics of the protein interaction network, the global and local characteristics of the entire protein interaction network and the internal structure of the protein complex cannot be considered at the same time, and the accuracy of protein complex identification is low.

Images