Method for recognizing protein network compound based on semantic density

A protein network and semantic density technology, applied in the field of data mining, can solve the problems of increasing the time complexity of the algorithm, and the modularity has a decomposition limit.

Inactive Publication Date: 2015-10-21
XIAN UNIV OF TECH
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Problems solved by technology

However, the evaluation function of the network structure often has certain limitations. For example, the modularity adopted by the hierarchical met...

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  • Method for recognizing protein network compound based on semantic density
  • Method for recognizing protein network compound based on semantic density
  • Method for recognizing protein network compound based on semantic density

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Embodiment Construction

[0043] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] Related concepts and definitions involved in a protein network complex recognition method based on semantic density of the present invention are as follows:

[0045] Gene Ontology database (Gene Ontology, GO) is a large-scale biological information database, and its goal is to unify the expression of all biological genes and protein properties. GO collects biological attributes as well as biochemical attributes to describe the function of the corresponding protein or its location in the cell. All attributes are divided into three categories: biological process, molecular function, and cellular component, abbreviated as P, F, and C, respectively. As an ontology, GO contains two semantic relations, namely "is_a" and "part_of". The directed acyclic graph (DAG) in the data structure is usually used to represent GO. The nodes in the graph...

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Abstract

The invention discloses a method for recognizing a protein network compound based on a semantic density, which is specifically implemented at the steps that all the protein properties in a network data set are searched in a gene ontology base GO as for a protein-protein interaction network data set without a weight; based on searching results, similarity of connected proteins in the network data set is calculated by a semantic similarity calculation method based on gene ontology; according to obtained similarity results, the given protein-protein interaction network data set is converted into an undirected network data set with a weight, wherein nodes represent the proteins, borders stand for interactions among the proteins, and the similarity among the proteins is the weight of the border; and the protein compound can be recognized from a protein-protein interaction network, wherein recognition accuracy is high and time complexity is low.

Description

technical field [0001] The invention belongs to the technical field of data mining methods, and relates to a method for identifying protein network complexes based on semantic density. Background technique [0002] There has been a long history of empirical research and theoretical simulation of complex networks, and many related techniques and methods derived from statistical physics and applied mathematics have been proposed. The concept of system networking has also been successfully applied to related research in molecular biology. Proteins in biological systems interact with each other to achieve a variety of molecular biological functions, and these interactions are referred to as PPIs (Protein-Protein Interactions). A biological system composed of proteins and their interactions can be formally described as an undirected graph, that is, protein interaction networks (PPI networks), referred to as protein networks. In protein networks, each node represents a protein, ...

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Application Information

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IPC IPC(8): G06F19/18
Inventor 周红芳段文聪郭杰王心怡何馨依刘杰李锦
Owner XIAN UNIV OF TECH
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