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Method for identifying driver gene from differential network

A driver gene and network technology, applied in the computer field, can solve problems such as insufficient accuracy of driver gene identification, failure to consider gene regulation network uncertainty, and inability to comprehensively measure network topology differences

Active Publication Date: 2020-10-23
SHANGHAI UNIV
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, these methods still have many unresolved problems when analyzing network differences, such as: (1) the uncertainty of edge connections in gene regulatory networks is not considered; (2) the network topology differences cannot be comprehensively measured; (3) driver gene identification Accuracy is not high enough

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  • Method for identifying driver gene from differential network
  • Method for identifying driver gene from differential network
  • Method for identifying driver gene from differential network

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Embodiment Construction

[0043] Preferred embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0044] In this embodiment, the DNF algorithm is implemented using C++ and R language, and the hardware environment of all experiments is a notebook computer with an Intel Core I5 ​​main frequency of 1.8GHz, 8GB of memory and a 64-bit Windows 10 operating system.

[0045] The first is the construction of the difference network model. From the computer point of view, given a gene regulatory network with n nodes, the network A_(i,j),i,j=1,...,n can be used to describe the relationship between any pair of genes. connection between. In a directed gene regulatory network, for a given gene i and gene j, their relationship in the network is described as A_(i,j)=A_(i→j)=c, which means a directed connection from gene i Extending to gene j, its action strength coefficient is c; in the undirected gene regulation network, for a given gene i and g...

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Abstract

The invention discloses a method for identifying a driver gene in a differential network. The method is characterized by comprising the following steps: giving two gene regulation and control networksrepresenting two biological states; quantifying the uncertainty of edges in the networks, then calculating the strongest and most stable regulation relationship between any two nodes in the networks;then characterizing and expressing the nodes as the distribution of network flows; and finally, quantifying the network flow distribution difference of each gene in the two networks so as to identifythe driver gene. The method is different from other existing difference network analysis methods, and has the main contribution that the difference of network topology is described by combining network flow and information entropy. The combination has the advantages that the uncertainty of edges in the gene regulation and control networks can be quantified, and meanwhile, comprehensive topological characteristics (local and global, linear and nonlinear characteristics) in the network can be captured, so higher driver gene recognition accuracy and robustness can be achieved.

Description

technical field [0001] The invention relates to the field of computers and proposes a differential network flow algorithm for identifying driving genes. Background technique [0002] Complex diseases and cell differentiation are a kind of complex dynamic life process. Deeply digging into its underlying life mechanism will provide a new level of solution for medical development. However, the current understanding of the underlying life mechanisms in these complex dynamic life processes is not clear enough. The complexity of this dynamic process is reflected in the fact that an unstable biological state reaches another stable biological state through multiple state transitions. Each transition process is driven by a group of key genes that drive the state transition. The key genes are called driver genes. Identifying the driving genes in complex diseases or dynamic processes such as cell differentiation is of great significance for the early diagnosis, treatment and prognosi...

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Application Information

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IPC IPC(8): G16B5/20G16B20/00
CPCG16B20/00G16B5/20Y02A90/10
Inventor 谢江杨伏长王娇
Owner SHANGHAI UNIV
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