Key protein identification method in network based on dynamic weighting interaction

Abstract

The invention discloses a key protein identification method use principle in a network based on dynamic weighting interaction. A key protein identification method comprises the steps of calculating aprotein activity time point and a protein activity probability, constructing a dynamic PPI network, then calculating a protein interaction weight according to the protein activity probability, and constructing a dynamic weighted PPI network; based on the established dynamic weighted PPI network, according to the topological characteristic and biological attribute of the protein network, calculating an edge clustering coefficient, a gene body similarity and a Pearson correlation coefficient between the interaction protein pairs; afterwards, obtaining an importance score, and performing arrangement according to the score value from largest to lowest, and outputting k proteins which correspond with the scores as a final result. The method according to the invention improves key protein identification efficiency and expands application range and practicability of the technique in a bioinformation field.

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 dynamic weighted interaction network, and particularly relates to a method for identifying key proteins in a dynamic weighted PPI network through network topology characteristics and protein biological attributes. Background technique [0002] In biological cells, key proteins are indispensable for the realization of cell functions, and the detection of key proteins helps to understand the laws of cell metabolism, growth and development. Therefore, the identification of key proteins has become one of the important research tasks in the field of proteomics. Although some achievements have been made in the identification of key proteins in protein interaction networks, due to the high complexity and randomness of living systems, effective methods in other fields often do not necessa...

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Problems solved by technology

However, there are often many such importance indicators, such as degree centrality, betweenness centrality, clustering centrality, etc. The disadvantages of identifying key proteins in this way are: (1) For a certain protein, its certain centrality (2) The key protein prediction method based on the topological properties of proteins not only depends on the accuracy of the protein interaction network, but also lacks consideration Biological properties of key proteins

(3) Only some properties of proteins under the static PPI network are considered, ignoring the fact that the protein interaction network changes over time

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