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A key protein identification method in an uncertain protein interaction network

A recognition method, protein technology, applied in proteomics, character and pattern recognition, genomics, etc., can solve the problems of ignoring the uncertainty of protein interaction network, false positives in protein interaction data, noise in PPI network, etc. , to achieve the effect of expanding the scope of application and practicability, overcoming negative effects, and improving efficiency

Pending Publication Date: 2019-04-26
YANGZHOU UNIV
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Problems solved by technology

[0003] Before the present invention was proposed, the field of identification of key proteins was initially identified by the topological features of the network, but the shortcomings of these methods for identifying key proteins were: (1) only considered the topological features of the network itself, and ignored Inherent Biological Characteristics of Proteins
(2) There is noise in the PPI network obtained through biological experiments, which makes the protein interaction data have false positives
(3) Neglecting the uncertainty of the protein interaction network

Method used

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  • A key protein identification method in an uncertain protein interaction network
  • A key protein identification method in an uncertain protein interaction network
  • A key protein identification method in an uncertain protein interaction network

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Embodiment

[0081] In order to verify the performance of the method (ETB-UPPI) proposed by the present invention, the present invention compares the quantity of key proteins identified with other various methods (DC, EC, SC, IC, NC, BC, CC, LAC, PeC, WDC , CoEWC, UDONC) were compared in four datasets (DIP, MIPS, Gavin, Krogan). For each method, the present invention selects the protein identification results of the top 1%, top 5%, top 10%, top 15%, top 20% and top 25% as candidate sets, and the proteins in each candidate set are compared with the key of the standard The protein sets are intersected to obtain the number of real key proteins in the candidate set. The experimental results are as follows figure 2 shown.

[0082] The prediction results of the DIP data set are as follows figure 2 shown. The method ETB-UPPI proposed by the present invention can obtain better results than other methods in identifying key proteins. When the top 1%, top 5%, top 10%, top 15%, top 20% and top 2...

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Abstract

SimRank is used for carrying out protein similarity calculation based on a key protein identification method in an uncertain protein interaction network. A SimRank calculation problem in an uncertainnetwork is converted into SimRank calculation in a deterministic network; and then, considering the topological characteristics of the protein interaction network and the biological characteristics ofthe protein, and calculating an edge clustering coefficient, gene ontology similarity, a Pearson's correlation coefficient and a subcellular localization score to obtain an importance score. And finally, ranking the scores from large to small, and outputting the first k proteins corresponding to the scores as final results. On the basis of an uncertain interaction network, the accuracy of key protein recognition is improved by fusing biological attributes and topological characteristics, meanwhile, the prediction result is more accurate, the prediction efficiency is improved, the applicationrange of the technology in the field of biological information is widened, and the practicability of the technology in the field of biological information is improved.

Description

technical field [0001] The invention belongs to the technical field of biological information, and mainly relates to a technology for identifying key proteins through Simrank similarity in an uncertain protein interaction network, in particular to a method for identifying key proteins by network topology information and protein biological attributes in an uncertain PPI network. Background technique [0002] Protein is one of the most basic substances in life and widely exists in various biological tissue cells. Key proteins are essential in living organisms. Removal of key proteins can lead to loss of function of the protein complex, making the organism unable to survive. Therefore, the prediction of key proteins plays an important role in understanding the minimum requirements of living cells and designing drugs. In addition, the identification of key proteins in the uncertain PPI network not only helps to understand the process of cell growth regulation, but also helps t...

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Application Information

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IPC IPC(8): G16B20/00G06K9/62
CPCG06F18/2321G06F18/22
Inventor 刘维马良玉何杰
Owner YANGZHOU UNIV
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