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
View PDF9 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • 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 technical problems of long experimental period, high equipment requirements and high cost of biological experiment methods for predicting the correlation between LncRNA and disease

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] see figure 1 , the Naive Bayesian-based LncRNA and disease association prediction method of the present embodiment, comprising the following steps:

[0037] S1: Construct a complex network based on the relationship between MiRNA and disease, MiRNA and LncRNA, and LncRNA and disease based on the known data sets. Step S1 comprises 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 duplicate data and erroneous data in the data set of the relationship between MiRNA and disease and the relationship between MiRNA and LncRNA.

[0040] S102: first unify the nomenclature of MiRNA, LncRNA and disease from different databases. Screen out the common MiRNA set in the relationship between MiRNA and disease and the relationship between MiRNA and LncRNA, and extract the relationship between common MiRNA and disease and the relationship between common MiR...

Embodiment 2

[0048] figure 2 It is a schematic flow chart of the naive Bayesian-based LncRNA-disease association prediction method in this embodiment, wherein: (A) collects and organizes the association relationship between MiRNA and disease from the three databases of HMDD, starBase and MNDR respectively , the relationship between MiRNA and LncRNA and the relationship between LncRNA and disease; (B) to construct 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) The network to be predicted is constructed 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 naive Bayesian-based LncRNA and disease association prediction method of the present embodiment comprises the following steps:

[0050] (1) Sorting and screening of da...

Embodiment 3

[0088] The Naive Bayesian-based LncRNA-disease association prediction system of the present invention includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and any of the above-mentioned methods is implemented when the processor executes the computer program A step of.

[0089] In summary, the present invention builds a complex network by integrating LncRN-MiRNA, disease-MiRNA, and LncRNA-disease associations, and then considers the association of the common neighbors (MiRNAs) nodes of the LncRNA-disease node pair in the network to the node pair Finally, based on the probability model of Naive Bayes, the prediction method is regarded as the probability that each neighbor node of the LncRNA node and the disease node will be connected to them. On the one hand, the naive Bayesian classifier is a very simple classifier with low computational complexity. On the other hand, the present invention is not entirely based on the known ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/22G06F19/24
Inventor 王雷喻景雯匡林爱冯湘轩占伟
Owner XIANGTAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap