Parameter-free nonlinear intelligent optimization method for identifying cancer driving pathway

An intelligent optimization and nonlinear technology, applied in the field of bioinformatics, can solve the problems of cumbersome, weak scalability, etc., to achieve the effect of more useful information, fast solution speed, high recognition efficiency and practical value

Pending Publication Date: 2022-02-08
GUANGXI NORMAL UNIV
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Problems solved by technology

[0004] Among the methods mentioned above, most methods try to use multi-omics data to reduce noise, reduce the negative impact on mutation data, and form identification models based on coverage and mutual exclusion, but some special cases cannot At the same time, most methods involve the setting of artificial parameters. In the application process, the value of artificial parameters needs to be determined through a large number of experiments, which will be cumbersome to use, so the scalability of these methods is relatively weak

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  • Parameter-free nonlinear intelligent optimization method for identifying cancer driving pathway
  • Parameter-free nonlinear intelligent optimization method for identifying cancer driving pathway
  • Parameter-free nonlinear intelligent optimization method for identifying cancer driving pathway

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Embodiment 1

[0065] A non-parametric nonlinear intelligent optimization method for identifying cancer-driving pathways, comprising the following steps:

[0066] 1) Set a nonlinear model without artificial parameters:

[0067] Using the existing somatic mutation matrix, copy number variation matrix and protein interaction network, where the somatic mutation matrix is ​​denoted as The copy number variation matrix is ​​denoted as The rows of matrix S and matrix C represent the same sample set P, and the columns represent the gene set G respectively S and G C , the value of each element in matrix S is S∈{0,1}, if gene j is mutated in patient i, then S ij = 1, otherwise S ij =0, the values ​​of the elements in the matrix C are C∈{-2,-1,0,1,2}, where the values ​​of the elements in the copy number variation matrix C are obtained by analyzing the GISTIC tool, set Q=(V,E) Represents a connected PPI network where each vertex v i ∈V denotes the gene g i expressed protein, each undirected ed...

Embodiment 2

[0111] Step 6) input mutation matrix A p×G , where |P|=90, |G|=440, PPI network Q, there are 9859 vertices and 40705 edges in the network, set the driving path size K=7, and then input CCA-NMWS related parameters: population size N= log2(|G|^K)*2=122, mutation probability P m1 = 0.5 and P m2 =0.8, maximum evolution generation maxg=1000, threshold maxt=10 to keep the optimal value constant; every subpopulation evolves T=10 generations to judge whether to compete, and the proportion of opponent set=0.2;

[0112] Step 8) Obtaining the size of the drive path of K=7, the rest of the steps are the same as in Embodiment 1, such as Figure 4 shown.

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Abstract

The invention discloses a parameter-free nonlinear intelligent optimization method for identifying a cancer driving pathway. The method comprises the following steps: 1) setting a non-linear model without subjective parameters; (2) setting a fitness function; (3) setting a crossover operator; (4) setting a mutation operator; (5) setting a cooperation strategy; (6) setting parameters; (7) constructing an initial population; and (8) executing iterative operation. The method can provide more useful information, is high in expansibility and practicability and high in solving speed, and can recognize more genes enriched on important driving pathways.

Description

technical field [0001] The invention relates to the field of bioinformatics and is used for identifying cancer driving pathways, in particular to a non-parametric nonlinear intelligent optimization method for identifying cancer driving pathways. Background technique [0002] With the continuous development of sequencing technology and the popularization of DNA sequencing technology, it is now possible to perform whole-genome sequencing of a large number of cancer somatic mutations, and many available databases have been generated based on second-generation sequencing technology, for example, The Cancer Genome Atlas (The Cancer Genome Atlas). Genome Atlas (TCGA for short), the International Cancer Genome Consortium (ICGC for short) and other large-scale cancer projects. A large number of useful data sets have been obtained through sequencing, and these data sets can support the work of finding driving pathways mentioned above. Based on these sequencing data sets, many resear...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/50G16B5/00G16B45/00
CPCG16B20/50G16B5/00G16B45/00
Inventor 陈小荣吴璟莉李高仕邓政朱凯
Owner GUANGXI NORMAL UNIV
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