Key-protein recognition method based on fusion of biological and topological features

A technology of topological features and identification methods, applied in the field of biological information, can solve the problems of lack of key protein biological characteristics, low accuracy of key proteins, dependence on the accuracy of protein interaction networks, etc., to expand the scope of application and practicability, and improve efficiency. , the effect of improving the accuracy

Active Publication Date: 2018-11-02
YANGZHOU UNIV
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Problems solved by technology

The disadvantages of these methods to identify key proteins are: (1) For a certain protein, some of its centralities are high, but other centralities may not be high, which will lead to low accuracy of key proteins identified; (2) Key protein prediction methods based on protein topological properties not only rely on the accuracy of protein interaction networks, but also lack of consideration of the biological characteristics of key proteins

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  • Key-protein recognition method based on fusion of biological and topological features
  • Key-protein recognition method based on fusion of biological and topological features
  • Key-protein recognition method based on fusion of biological and topological features

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Embodiment

[0077] The method proposed in this paper (Co-MTB) will be compared with the existing methods of DC, EC, BC, NC, LBCC, SON, COEWC in DIP dataset and MIPS dataset. For each method, we select the top 100 to 600 protein results as the candidate set.

[0078] The prediction results of the DIP data set are as follows Figure 2a , Figure 2b , Figure 2c , Figure 2d , Figure 2e , Figure 2f shown. The method Co-MTB proposed in this paper can achieve better results than other methods in identifying key proteins. When taking the top 100-600 proteins as candidate proteomes, the prediction accuracy of Co-MTB is 25%, 19%, 23%, 24%, 24%, 28% higher than CoEWC, respectively. However, Co-MTB improves the prediction accuracy by 11%, 8%, 8%, 6%, 7% and 5% at six levels compared with the recently developed method SON.

[0079] The prediction results of the MIPS data set are as follows Figure 3a , Figure 3b , Figure 3c , Figure 3d , Figure 3e , Figure 3f shown. The algorit...

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Abstract

The invention belongs to the field of bioinformatics technology, and particularly relates to a key-protein recognition method based on fusion of biological and topological features. The method includes: assigning a score, which indicates an importance degree of each protein vertex, to the protein vertex, wherein the scores of all the vertices constitute a vector of n columns; giving initial valuesof the scores, constituting the attribute values of the protein vertices according to biological information and values of topological characteristics, and constituting an attribute matrix; and finally, carrying out descending arrangement according to the scores, and outputting k proteins corresponding to the scores, namely final results. Combination of the topological characteristics of a protein interaction network and the protein biological-attributes facilitates identification of key proteins, and improves efficiency of key-protein recognition.

Description

technical field [0001] The invention belongs to the technical field of biological information, and mainly relates to a technology for identifying key proteins in a protein interaction network by fusing biological and topological features, in particular to a method for identifying key proteins in a PPI network based on network topology information and protein biological attributes. Background technique [0002] In biological cells, key proteins are indispensable for the realization of cell functions, and have important application value for the survival of organisms, drug target design, disease treatment and prediction, etc. 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 necessarily ach...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/18G06F19/24
Inventor 刘维马良玉陈昕
Owner YANGZHOU UNIV
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