Method and system for predicting cancer

A cancer, preset type technology, applied in the fields of bioinformatics and computational biology, can solve problems such as poor classification effect, and achieve the effect of improving accuracy, efficient prediction, and efficient classification

Active Publication Date: 2020-11-06
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a method and system for predicting cancer to solve the current technical problem of poor classification effect obtained by relying on the feature data of gene expression profile data obtained by a single feature extraction method

Method used

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  • Method and system for predicting cancer
  • Method and system for predicting cancer
  • Method and system for predicting cancer

Examples

Experimental program
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Effect test

no. 1 example

[0045] This embodiment provides a method for predicting cancer, which can be implemented by an electronic device, and the electronic device can be a terminal or a server. The execution flow of this method is as follows figure 1 shown, including the following steps:

[0046] S101, performing difference analysis on gene expression profile data between cancer patients and normal people, and obtaining differential genes between cancer patients and normal people;

[0047] It should be noted that the data basis of this example is the expression profile data of differential genes between cancer patients and normal people. For this reason, this example uses the limma package in R language to realize the gene expression profile data of cancer patients and normal people. Differential expression analysis. limma (Linear Models for Microarray Data) is a robust T-test method based on empirical Bayesian, which has been implemented in the limma package of Bioconductor. The limma method is ...

no. 2 example

[0100] This embodiment provides a system for predicting cancer, and the system for predicting cancer includes the following modules:

[0101] The differential gene acquisition module is used for differential analysis of gene expression profile data between cancer patients and normal people, and obtains differential genes between cancer patients and normal people;

[0102] The feature data acquisition module is used to analyze the gene expression profile data of cancer patients and normal people based on weighted gene co-expression network analysis to obtain hub genes; Process the gene expression profile data of differential genes to obtain dimensionality reduction data;

[0103] The classification module is used to use the gene expression profile data and dimensionality reduction data of the hub genes acquired by the feature data acquisition module as the classification features of the preset type of cancer classifier, so as to realize the relationship between cancer patients ...

no. 3 example

[0106] This embodiment provides an electronic device, which includes a processor and a memory; at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor, so as to implement the method of the first embodiment.

[0107] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein at least one instruction is stored in the memory, so The above instructions are loaded by the processor and perform the following steps:

[0108] S101, performing difference analysis on gene expression profile data between cancer patients and normal people, and obtaining differential genes between cancer patients and normal people;

[0109] S102, analyze the gene expression profile data of cancer patients and normal people based on weighted gene co-expression network analysis to obtain hub genes; and p...

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Abstract

The invention discloses a method and system for predicting cancer. The method comprises the following steps: carrying out difference analysis on the gene expression profile data of a cancer patient and a normal person to obtain difference genes; analyzing the gene expression profile data of the cancer patient and the normal person based on weighted gene co-expression network analysis to obtain a hub gene; processing the gene expression profile data of the difference genes through a variational auto-encoder algorithm to obtain dimension reduction data; and with the gene expression profile dataof the hub gene and the dimension reduction data as classification features of a cancer classifier of a preset type, conducting accurate classification of cancer patients and normal people through thecancer classifier. According to the method and the system for predicting cancer, the gene expression profile data of the hub gene obtained by weighted gene co-expression network analysis and the dimension reduction data processed by the variational auto-encoder are jointly used as the classification characteristics of the cancer classifier, so the accuracy of the cancer classifier is effectivelyimproved, and the purpose of efficiently predicting cancer is achieved.

Description

technical field [0001] The invention relates to the technical fields of bioinformatics and computational biology, in particular to a method and system for predicting cancer based on a variational autoencoder and a weighted gene co-expression network. Background technique [0002] Colorectal cancer (CRC) is a malignant tumor with the third highest incidence and the second highest mortality worldwide. Despite the continuous advancement of medical technology, most CRC patients are already in the middle and advanced stages when they are hospitalized due to pain. Cancer prognostic markers are of great significance for the early diagnosis of cancer, and precision medicine requires classification models for accurate screening and diagnosis. To improve the accuracy of predicting CRC from microarray gene expression datasets, the feature extraction method is a key factor affecting the performance of classifiers. [0003] There are many feature extraction methods for gene expression p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16H50/70G06K9/62G06F16/36
CPCG16H50/30G16H50/70G06F16/367G06F18/23G06F18/2411
Inventor 艾冬梅王瑜多潘鸿飞
Owner UNIV OF SCI & TECH BEIJING
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