Method for identifying key protein based on genetic algorithm in PPI network

Abstract

The invention relates to an algorithm for identifying a key protein based on a genetic algorithm in a PPI network. The algorithm comprises the steps of generating an initial population in a protein interaction network; calculating the fitness of individuals; performing selection operation by a roulette wheel method; performing crossing operation and mutation operation among the randomly selected individuals; and performing local optimization on multiple individual solutions. According to the algorithm, the defects of existing methods are overcome; indexes are optimized and bio-information is fused, so that the reliability is higher and lots of unnecessary calculations are reduced; and the predicted key protein can be locally optimized, so that the efficiency of key protein identification is improved, and the application range and practicality of the technology in the field of the bio-information are expanded and improved.

Description

technical field [0001] The invention belongs to the technical field of biological information, mainly relates to a technology for identifying key proteins in a protein interaction network through a genetic algorithm, in particular to an algorithm for identifying key proteins in a PPI network based on a genetic algorithm. Background technique [0002] Key proteins refer to those proteins that are necessary for living organisms, and knocking out key proteins may lead to the inability of living organisms to survive. The identification of key proteins has important application value in the aspects of organism survival, drug target design, and disease treatment. [0003] In the field of key protein identification, it was initially identified through biological experiments, such as single gene knockout, but it required a lot of manpower, material resources and time. With the development of high-throughput technologies, such as yeast two-hybrid, tandem affinity purification, etc.,...

AI Technical Summary

Problems solved by technology

However, most of these methods only consider the topology of the protein interaction network, without considering biological information, or only consider one kind of biological information, resulting in low accuracy of identified key proteins

[0004] Before the present invention was made, in the existing methods, most of the key proteins were to be calculated, and then among the identified key proteins, P ones with a higher degree were taken. The disadvantage of identifying key proteins like this is: ( 1) In practical applications, we only care about which P indicators are larger and have a high degree of criticality, and there is no need to increase the amount of calculation to calculate certain indicators of proteins one by one, and then sort them, and take the larger P ones

(2) As far as a single protein is concerned, a certain index is relatively high, but as far as the P with the highest index is concerned, its criticality may not be the highest in the overall PPI network, and it may only be a representative of a certain local vertex, especially some Using a local link index, or using an algorithm that gradually expands the connection closeness, it is easier to lead to the locality of the optimal solution

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