Method and system for predicting association relationship between disease and LncRNA

A related relationship and disease technology, applied in the field of bioinformatics disease prediction, can solve the problems of high time and cost of biological experiments, achieve the effect of reducing the cost and overhead of biological experiments and accelerating research progress

Active Publication Date: 2019-01-18
CHANGSHA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In order to solve the existing technical problems of high time and cost of biological experi

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  • Method and system for predicting association relationship between disease and LncRNA
  • Method and system for predicting association relationship between disease and LncRNA
  • Method and system for predicting association relationship between disease and LncRNA

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Experimental program
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Embodiment 1

[0056] The method for predicting the relationship between disease and LncRNA in this embodiment comprises the following steps:

[0057] S1: Obtain the LncRNA-miRNA association relationship and miRNA-disease association relationship from known databases, and construct the LncRNA-miRNA-disease interaction network based on the two. details as follows:

[0058] S11: Obtain the LncRNA-miRNA association relationship data set from the known database, and obtain the LncRNA-miRNA interaction pair;

[0059] S12: Obtain miRNA-disease association data sets from known databases, and obtain miRNA-disease interaction pairs;

[0060] S13: Construct a LncRNA-miRNA association network based on LncRNA-miRNA interaction pairs;

[0061] S14: Construct a miRNA-disease association network based on miRNA-disease interaction pairs;

[0062] S15: Based on the LncRNA-miRNA association network and the miRNA-disease association network constructed above, construct an LncRNA-miRNA-disease interaction ne...

Embodiment 2

[0078] figure 2 Is the overall flowchart of the present embodiment, wherein, MD is the adjacency matrix of miRNA-disease, LM is the adjacency matrix of LncRNA-miRNA, SIP (L) is the super expression spectrum matrix of LncRNA, SIP (D) is the super expression of disease Spectrum matrix, NSL is the similarity matrix of LncRNA, NSD is the similarity matrix of disease, LMD=LM*MD, LMD T is the transpose of the LMD matrix, and S(i,j) is the prediction score of the i-th disease and the j-th LncRNA.

[0079] This embodiment provides a method for predicting the relationship between disease and LncRNA based on the LncRNA-miRNA-disease interaction network, including the following steps:

[0080] S1: Acquisition and processing of raw data sets, preprocessing and feature selection of data features. details as follows:

[0081] S11: Acquisition of LncRNA-miRNA association relationship;

[0082] Two versions of the LncRNA-miRNA association dataset (version 2015 and version 2017) were down...

Embodiment 3

[0154] As a general technical concept, this embodiment also provides a system for predicting the relationship between diseases and LncRNAs corresponding to the methods in the above embodiments, including: a network construction unit for obtaining LncRNA-miRNA associations from known databases Relationship and miRNA-disease association relationship, construct LncRNA-miRNA-disease interaction network according to the two; expression profile construction unit, used to construct disease super-expression profile and LncRNA super-expression based on the LncRNA-miRNA-disease interaction network Spectrum; Model construction unit, for according to the super expression profile of described disease and LncRNA, adopt the LncRNA similarity calculation and disease similarity calculation based on RBF neural network, train the prediction model of disease and LncRNA correlation; Prediction unit , for predicting LncRNA-disease association pairs of candidate samples using the predictive model.

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Abstract

The invention discloses a method and a system for predicting the association relationship between a disease and LncRNA. The method comprises the steps: the LncRNA-miRNA association relationship and the miRNA-disease association relationship are obtained from a known database, and a LncRNA-miRNA-disease interaction network is constructed according to the two relationship; a disease hyper-expressionprofile and a LncRNA hyper-expression profile are constructed based on the LncRNA-miRNA-disease interaction network; the prediction model of the disease and LncRNA association relationship is trainedaccording to the disease hyper-expression profile and the LncRNA hyper-expression profile by using LncRNA similarity computing and disease similarity computing based on the RBF neural network; and the LncRNA-disease association pairs of the candidate samples are predicted by using the prediction model. The most promising LncRNA disease association for further experimental verification is provided, the potential disease-related LncRNA can be effectively mined from the mass biological data, the cost and the expense of the biological experiment can be reduced and the research progress in the bioinformatics field can be accelerated.

Description

technical field [0001] The present invention relates to the field of bioinformatics disease prediction, in particular to a method for predicting the relationship between disease and LncRNA based on LncRNA (Long non-coding RNA, long-chain non-coding RNA)-miRNA (MicroRNA, microRNA)-disease interaction network and system. Background technique [0002] Long non-coding RNA (LncRNA) is a kind of important non-coding RNA with a length of more than 200 nucleotides. Plays a key role in many important biological processes, including transcription, translation, splicing, differentiation, epigenetic regulation, immune response, and cell cycle regulation, among others. Therefore, the mutation and dysregulation of lncRNAs have an important relationship with many human diseases. For example, studies have shown that LncRNAs are closely related to cancer, cardiovascular diseases, and neurological diseases. Clinical experiments have shown that some LncRNAs, such as HOTAIR (HOX tran antisens...

Claims

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Application Information

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IPC IPC(8): G16B40/00G16B50/30G16H50/20G16H50/70
CPCG16H50/20G16H50/70
Inventor 王雷轩占伟匡林爱李学勇陈治平谭义红
Owner CHANGSHA UNIVERSITY
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