LncRNA and disease association relationship prediction method and system based on Naive Bayes

A technology of correlation relationship and prediction method, applied in the field of correlation prediction in bioinformatics, can solve the problems of high equipment requirements, high cost, long experimental period, etc., and achieve the effect of reducing workload and improving prediction effect.

Active Publication Date: 2018-11-30
XIANGTAN UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a naive Bayesian-based method and system for predicting the correlation between LncRNA and disease, so as to solve the t

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  • LncRNA and disease association relationship prediction method and system based on Naive Bayes
  • LncRNA and disease association relationship prediction method and system based on Naive Bayes
  • LncRNA and disease association relationship prediction method and system based on Naive Bayes

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Example Embodiment

[0035] Example 1:

[0036] See figure 1 The method for predicting the association relationship between LncRNA and disease based on Naive Bayes of this embodiment includes the following steps:

[0037] S1: Construct a complex network based on the association relationship between MiRNA and disease, the association relationship between MiRNA and LncRNA, and the association relationship between LncRNA and disease. Step S1 includes the following steps:

[0038] S101: Download from multiple known databases: the relationship between MiRNA and disease and the relationship between MiRNA and LncRNA;

[0039] Delete the duplicate data and wrong data in the data set of the relationship between MiRNA and disease and the relationship between MiRNA and LncRNA.

[0040] S102: First unify the naming of MiRNA, LncRNA and disease from different databases. Screen the shared MiRNA set in the relationship between MiRNA and disease and the relationship between MiRNA and LncRNA, and extract the relationship ...

Example Embodiment

[0047] Example 2:

[0048] figure 2 It is a schematic flow chart of the method for predicting the association relationship between LncRNA and disease based on Naive Bayes in this embodiment, in which: (A) is the collection of the association relationship between MiRNA and disease from the three databases of HMDD, starBase and MNDR. , The relationship between MiRNA and LncRNA and the relationship between LncRNA and disease; (B) is to build a complex network by integrating the relationship between MiRNA and disease, the relationship between MiRNA and LncRNA, and the relationship between LncRNA and disease; (C) Construct the network to be predicted by expressing the network in the form of an adjacency matrix; (D) Calculate the similarity value of the final lncRNA-disease node pair based on the contribution of different neighbor nodes.

[0049] The method for predicting the association relationship between LncRNA and disease based on Naive Bayes of this embodiment includes the followi...

Example Embodiment

[0087] Example 3:

[0088] The system for predicting the association relationship between LncRNA and disease based on Naive Bayes of the present invention includes a memory, a processor, and a computer program that is stored in the memory and can run on the processor. The processor implements any of the above methods when the computer program is executed. A step of.

[0089] In summary, the present invention constructs a complex network by integrating LncRN-MiRNA, disease-MiRNA, and LncRNA-disease association relationships, and then considers the association relationship between the LncRNA-disease node pairs in the network and the common neighbor (MiRNAs) nodes of the node pair. Finally, based on the Naive Bayesian probability model, the prediction method is regarded as the probability that each neighbor node of the LncRNA node and the disease node connects them. On the one hand, the naive Bayes classifier is a very simple classifier with low computational complexity. On the other...

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Abstract

The invention discloses an LncRNA and disease association relationship prediction method based on Naive Bayes, which comprises: according to known data sets of an MiRNA and disease association relationship, an MiRNA and LncRNA association relationship and an LncRNA and disease association relationship, constructing a complex network based on the three association relationships; in the complex network, finding common neighbor nodes of LncRNA nodes and disease nodes; by a probability model based on Naive Bayes, calculating a probability that the LncRNA nodes and the disease nodes, which have thecommon neighbor nodes, are connected so as to obtain a similarity value of LncRNA node and disease node pairs. According to the invention, a plurality of association relationships of a plurality of databases are integrated so as to establish more connections for the to-be-predicted LncRNA nodes and disease nodes in the constructed complex network, thereby improving a prediction effect of the LncRNA and disease association relationship.

Description

technical field [0001] The present invention relates to the field of association prediction in bioinformatics, in particular to a method and system for predicting associations between LncRNA (Long Noncoding RNA, long-chain non-coding RNA) and diseases based on Naive Bayesian. Background technique [0002] Only about 1.5% of the human genome is responsible for protein-coding genes, which means that more than 98% of the human genome does not encode protein sequences. Studies have found that LncRNA plays an important role in human physiological changes and various complex human diseases (lung cancer, colon cancer, Alzheimer's disease, etc.), such as genome imprinting, cell differentiation variation, immune response, tumorigenesis etc. In particular, it is of great theoretical and practical significance to develop a suitable computational model to predict the association between LncRNA and disease based on various biological association data sets. In recent years, more and mor...

Claims

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Application Information

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IPC IPC(8): G06F19/22G06F19/24
Inventor 王雷喻景雯匡林爱冯湘轩占伟
Owner XIANGTAN UNIV
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