Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Prediction method of miRNA target genes based on convolutional neural network

A target gene prediction, convolutional neural network technology, applied in the field of bioinformatics, can solve problems such as data set imbalance

Active Publication Date: 2019-07-16
SUN YAT SEN UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a method for predicting miRNA target genes based on convolutional neural networks, which not only avoids false filtering of real targets that do not meet certain feature thresholds, but also solves the imbalance problem of experimental verification data sets

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
  • Prediction method of miRNA target genes based on convolutional neural network
  • Prediction method of miRNA target genes based on convolutional neural network
  • Prediction method of miRNA target genes based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Such as Figure 1-4 Shown, a kind of miRNA target gene prediction method based on convolutional neural network, comprises the following steps:

[0037] S1: According to the published miRNA-mRNA pair, download the corresponding sample data mRNA required for the experiment from the NCBI library, download the corresponding sample data miRNA required for the experiment from the miRBase library, and calculate the eigenvalues ​​of the original and negative samples, where , the characteristics are divided into three categories: complementarity, accessibility, and conservation; and complementarity can be evaluated from 9 characteristics, 9 characteristic values; accessibility can be evaluated from 8 characteristics, 8 Eigenvalues; Conservatism is evaluated from 3 eigenvalues, 3 eigenvalues; therefore a total of 20 eigenvalues ​​need to be calculated;

[0038] S2: Construct a balanced data set: In order to obtain more candidate sites, set loose thresholds for the three characte...

Embodiment 2

[0053] Concrete steps of the miRNA target gene prediction method based on convolutional neural network of the present invention:

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 provides a new algorithm (CNNmiRT) to predict miRNA target genes by utilizing the features of complementation, conservation and accessibility between miRNA-target genes. Because experiment support on negative interaction is not published commonly, and is not recorded in a database, therefore the number of loci of verified negative samples is far lower than that of loci of positive samples. A restraint release method is used to build four kinds of balanced experimentally-verified training data sets in order to compensate, and the four kinds of balanced experimentally-verified training data sets comprise one highly-conservative positive sample data set, one completely-complementary positive sample data set, one accessible positive sample data set and one negative sample data set. The method not only avoids wrong filtering of real targets which do not meet certain feature thresholds, but also solves the problem of disequilibrium of the experimentally-verified data sets. Then, the miRNA target genes are predicted by applying the convolutional neural network.

Description

technical field [0001] The present invention relates to the field of bioinformatics, more specifically, to a method for predicting miRNA target genes based on convolutional neural networks. Background technique [0002] With the rapid development of bioinformatics, genomics has become a way for people to study the causes of diseases from the perspective of the origin of genes, and the core principle of the research is the central dogma. The central dogma refers to the process in which genetic information is transferred from DNA to RNA through transcription, and then translated into protein by RNA. Genetic information flows from DNA to RNA and then to protein, so it is generally said that this is the process of DNA expression, but as Lee et al in 1993 (Lee R C, Feinbaum R L, Ambros V. The C. elegans heterochronic gene lin- 4 encodes small RNAs with antisense complementarity to lin-14[J].Cell, 1993, 75(5): 843-854.) The discovery of miRNA has changed people’s understanding of...

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): G16B40/00G16B30/00G06N3/04
CPCG16B40/00G16B20/00G06N3/045Y02A90/10
Inventor 万天根龙冬阳
Owner SUN YAT SEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com