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Protein composite identification method based on random walking model

A protein complex and random walk model technology, applied in proteomics, genomics, bioinformatics, etc., can solve the problem of protein data sets containing false negatives

Inactive Publication Date: 2017-01-25
SHANGHAI DIANJI UNIV
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Problems solved by technology

[0008] The purpose of the present invention is to solve the problem of false-negative and false-positive noise data contained in protein data sets, and to overcome the limitations of using graphical models to find protein complex structures fixed

Method used

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  • Protein composite identification method based on random walking model
  • Protein composite identification method based on random walking model
  • Protein composite identification method based on random walking model

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Embodiment Construction

[0029] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0030] combine image 3 , the present invention relates to a protein complex identification method based on a random walk model, comprising the following steps:

[0031]The first step is to perform a restart-type random walk on the entire weighted protein interaction network, realize the ordering of each protein node according to the importance of the protein interaction network, filter out unimportant protein nodes, and retai...

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Abstract

The invention provides a protein composite identification method based on a random walking model. Interaction data and false-negative or false-positive noisy data truly existing on a protein network are forecasted through the random walking algorithm. On the protein interaction network obtained after false-negative data and false-positive noisy data are removed, protein composites with the biological significance are identified through a H-index graph model, the semantic similarity between the protein composites is calculated according to a GO body, and the identified protein composites are finally determined. According to the protein composite identification method based on the random walking model, the algorithm is insensitive to input parameters, and the effectiveness of the provided algorithm is verified through experiments.

Description

technical field [0001] The invention relates to a method for removing false negative or false positive noise data on a protein interaction network, so as to predict the real interaction data on the protein network. Background technique [0002] In the post-gene era, protein has once again become a research hotspot for biological scientists. The sequence information of the whole genome cannot fully explain and speculate on various life phenomena of cells. Protein is the ultimate executor of cell activity and function. Therefore, identifying the interactions between various proteins in cells is beneficial to understanding various physiological processes in biological cells. So far, many experimental methods have been used to identify protein interactions, and these methods can be roughly divided into two categories: small-scale (low-throughput) methods and large-scale (high-throughput) methods. [0003] When the protein network is large, the number of protein pairs to be dete...

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Application Information

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IPC IPC(8): G06F19/18
CPCG16B20/00
Inventor 林志杰
Owner SHANGHAI DIANJI UNIV
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