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Method and system for multidimensional target prediction of disease-associated non-coding RNA regulatory axis

A prediction method and technology of regulating axis, which can be applied in the fields of biostatistics, bioinformatics, instruments, etc., can solve the problem of inability to identify functional modules of lncRNA-miRNA-mRNA regulatory axis, and achieve the effect of improving reliability.

Active Publication Date: 2022-03-08
SHANDONG UNIV QILU HOSPITAL
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although many methods have been developed for predicting disease-associated ncRNAs, such as RWR, RWRHLD, LncRDNetFlow, and LncPriCNet, it is still not possible to identify the lncRNA-miRNA-mRNA regulatory axis as a complete functional module

Method used

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  • Method and system for multidimensional target prediction of disease-associated non-coding RNA regulatory axis
  • Method and system for multidimensional target prediction of disease-associated non-coding RNA regulatory axis
  • Method and system for multidimensional target prediction of disease-associated non-coding RNA regulatory axis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] This Example 1 provides a newly optimized xgboost algorithm and multi-organized data integration analysis in an analytical application in endogenous competition non-encoded RNA regulatory networks of complex diseases, specifically divided into the following steps:

[0043] Step 1: Data acquisition and pretreatment. By pretreatment of corresponding bioinformatics analysis of at least three dimensions (e.g., genomic, transcription group and methylation data), the weight gene co-expression network analysis screening out of the disease group and the control group Difference expression gene and co-expression gene. The resulting results of the screening were enriched, and the protein-protein interaction (PPI) network was constructed, and the composition of the hub gene was finally determined.

[0044] In this Example 1, a lot of difference expression genes were obtained when differential expression analysis, and these genes were difficult to find the relationship between these ge...

Embodiment 2

[0060] In this Example 2, a disease-associated non-encoded RNA regulating axis multidimensional target prediction system, including:

[0061] Screening modules for screening the differential expression genes and co-expression gene modules between disease groups and control groups using multi-dimensional component data, and perform enrichment analysis;

[0062] The module is determined, and the constructed protein-protein interaction network is used to treat the screened differentially expression gene and co-expression gene to determine the composition of the hub gene;

[0063] Get the module for acquiring key protein coding markers in the determined hub gene;

[0064] The prediction module is used to extract a non-encoded RNA regulating axis network including the key protein coding marker using the constructed competitive endogenous RNA network.

[0065] In this Example 2, a multi-dimensional target prediction method of complex disease-related non-encoded RNA regulatory shaft is r...

Embodiment 3

[0081] Such as figure 1 As shown in the present embodiment, a multi-dimensional target-based non-encoded RNA regulating axis prediction method based on an optimized machine learning XGBOOST algorithm is provided, and the implementation of the following three steps:

[0082] Step 1: Screening the potential difference biomarkers through multiple groups of student information databases;

[0083] The specific steps include: Download disease-related genome, transcription group and methylation data via the GEO database. For example, Download transcription group data (GSE154377, GSE150621), expression profile data (GSE87295) and methylation data (GSE88929), and gene expression synthesis (GSE 112168) (GSE 112168) .

[0084] The above data is pretreated to retain appropriate data of differentially expressing genes, methylated genes, and miRNA. According to the T-SNE algorithm and the relevant matrix analysis, the sample of the Diabetic Group and the control group in the control group was r...

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Abstract

The invention provides a method and system for multi-dimensional targeting prediction of disease-related non-coding RNA regulatory axis, which belongs to the technical field of biological information processing based on machine learning, and uses multi-dimensional omics data to screen out the differentially expressed genes and co-expressed genes between the disease group and the control group. Express gene modules and perform enrichment analysis; based on the constructed protein-protein interaction network, process the screened differentially expressed genes and co-expressed genes to determine the composition of hub genes; among the identified hub genes, obtain key proteins Coding markers: using the constructed competitive endogenous RNA network, extract the non-coding RNA regulatory axis network including the key protein coding markers. The present invention can effectively predict complex disease-related endogenous competitive non-coding RNA regulatory network, and identify the key lncRNA-miRNA-mRNA regulatory axis, which helps to provide more promising candidates for the study of molecular pathogenic mechanisms of complex diseases Or, provide potential molecular markers for the development of precision medicine.

Description

Technical field [0001] The present invention relates to the field of biological information processing techniques based on machine learning, and more particularly to an optimized xgboost algorithm and a multi-organized data integrated analysis of non-encoded RNA regulating axis multidimensional target prediction methods and systems. Background technique [0002] Gene expression is a process of synthesizing a functional gene product from a gene's genetic information, which is affected by the precise regulation of multiple dimensions and complex interactions, such as gene mutation, transcription factors, non-encoded RNA, and methylation. This multi-level regulatory network enables multi-class integration into an important method for characterizing the complex biological mechanism of phenotype. [0003] The emergence of high-throughput sequencing technology and multi-class technology has driven a large number of multi-organized data, which includes not only different data with diffe...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B35/20G16B40/00G16B50/00
CPCG16B35/20G16B40/00G16B50/00
Inventor 孙宇官方霖严江伟申忱李慧宇
Owner SHANDONG UNIV QILU HOSPITAL
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