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A method to identify cancer-driving pathways

A cancer and pathway technology, applied in genomics, instrumentation, proteomics, etc., can solve problems such as low efficiency and achieve fast, efficient, and high-speed solutions

Active Publication Date: 2022-04-05
GUANGXI NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Therefore, a new omics integration method is needed to fuse each data, instead of calculating them separately when calculating the weight of the driving pathway, and designing a reasonable calculation model to calculate the weight of the driving pathway, and the previous algorithm has a large data size. In the case of , the efficiency is not very high, so a more efficient algorithm is designed to solve the calculation model to solve the shortcomings of the existing methods

Method used

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  • A method to identify cancer-driving pathways
  • A method to identify cancer-driving pathways
  • A method to identify cancer-driving pathways

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] A method of identifying cancer-driving pathways, comprising the steps of:

[0049] 1) Construct a weighted non-binary mutation matrix:

[0050] Existing Glioblastoma GBM Somatic Mutation Matrix copy number variation matrix and gene expression matrix medium mutation matrix copy number variation matrix and gene expression matrix The middle row represents the same sample set p of one cancer, and the columns represent the gene set G respectively S , G C and G E , in the matrix in, s ij ∈ {0, 1} (i=1, 2, ..., |p|, j = 1, 2, ..., |G S |), j gene mutation in sample i, s ij The value is 1, otherwise the value is 0; the matrix Each element c in ij ∈ {-2, -1, 0, 1, 2} (i=1, 2, ..., |p|, j = 1, 2, ..., |G C |), represents the copy number variation value of gene j in sample i; in the matrix Middle e ij ∈R(i=1, 2,..., |p|, j=1, 2,..., |G E |), represents the expression level of gene j in sample i; let the matrix The gene set G in A =G S ∪G C , the samp...

Embodiment 2

[0079] In this example step 1) set λ 1 = 3 and λ 2 =7, construct weighted non-binary mutation matrix A |p|×|G| , where |p|=90, |G|=920;

[0080] This example step 7) input weighted non-binary mutation matrix A |p|×|G| , where |p|=90, |G|=920, the model in formula (2), the size of the driving pathway to be found is k=10, and the CGA-MWS related parameters are set to population size P=460, mutation probability P m =0.3, the maximum evolution algebra maxstep=1000, the threshold maxt=10 for keeping the optimal value constant;

[0081] This example step 9) obtains the driving path that the size is k=10, and the operation diagram is as follows figure 2 shown.

[0082] All the other steps are the same as in Example 1.

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Abstract

The invention discloses a method for identifying cancer driving pathways, comprising the following steps: 1) constructing a weighted non-binary mutation matrix; 2) setting a recognition model; 3) setting a fitness function; 4) setting a crossover operator; 5) Set the mutation operator; 6) Set the cooperation strategy; 7) Set the parameters; 8) Construct the initial population; 9) Perform iterative operations. This method can provide more useful information, has strong scalability, high speed, fast solution speed, and contains many genes enriched in important driving pathways.

Description

technical field [0001] The invention relates to the field of identification of cancer driving pathways, in particular to a method for identifying cancer driving pathways. Background technique [0002] With the rapid development of deep sequencing technology, in recent years, large-scale cancer projects such as The Cancer Genome Atlas (TCGA for short) and the International Cancer Genome Consortium (ICGC for short) have provided a large number of cancer Multi-omics data. Designing efficient computational methods to identify "driver mutations" in cancer development has become a hotspot in several past studies. However, most methods cannot determine the heterogeneity of genetic mutations, that is, the resulting mutated genes may also differ between different samples from the same cancer. The researchers found that there is a high probability that different mutated genes target the same biological pathway, and found that the development of cancer is essentially controlled by bi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B20/10G16B20/50
CPCG16B20/10G16B20/50
Inventor 朱凯吴璟莉李高仕
Owner GUANGXI NORMAL UNIV