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RNA binding protein prediction method and device based on multi-scale attention convolutional neural network

A convolutional neural network and prediction method technology, applied in biological neural network models, neural architecture, proteomics, etc., can solve problems such as low prediction accuracy, achieve faster convergence, improve prediction accuracy, and improve robustness Effect

Active Publication Date: 2020-10-20
WUHAN UNIV
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  • Application Information

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Problems solved by technology

[0010] The present invention proposes a method and device for predicting RNA-binding proteins based on a multi-scale attentional convolutional neural network, which is used to solve or at least partially solve the technical problem of low prediction accuracy existing in methods in the prior art

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  • RNA binding protein prediction method and device based on multi-scale attention convolutional neural network
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  • RNA binding protein prediction method and device based on multi-scale attention convolutional neural network

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Embodiment 1

[0049] This embodiment provides a method for predicting RNA-binding proteins based on a multi-scale attention convolutional neural network, the method comprising:

[0050] S1: Obtain RNA data and perform preprocessing;

[0051] S2: Encoding the preprocessed RNA data to construct network training samples;

[0052] S3: Construct a multi-scale attention convolutional neural network, wherein the multi-scale attention convolutional neural network includes multiple branches, and each branch is set with a convolution kernel of a different size to learn different scales in the RNA data. Features, and introduce the channel attention mechanism to learn the importance of different channels in classification. When identifying RNA binding sites, the convolution kernels of different channels correspond to different binding site structures;

[0053] S4: Input the network training samples into the constructed multi-scale attention convolutional neural network, and use the Adam optimization m...

Embodiment 2

[0102] Based on the same inventive concept, the second aspect of the present invention provides a device for predicting RNA-binding proteins based on a multi-scale attention convolutional neural network, the device comprising:

[0103] A preprocessing module for obtaining RNA data and performing preprocessing;

[0104] The encoding module is used to encode the preprocessed RNA data to construct a network training sample;

[0105] The network building block is used to construct a multi-scale attention convolutional neural network, wherein the multi-scale attention convolutional neural network includes multiple branches, and each branch is provided with convolution kernels of different sizes for learning in RNA data respectively. The features of different scales, and introduce the channel attention mechanism to learn the importance of different channels in classification. When identifying RNA binding sites, the convolution kernels of different channels correspond to different bi...

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Abstract

The invention discloses an RNA binding protein prediction method based on a multi-scale attention convolutional neural network. The RNA binding protein prediction method comprises a training stage anda prediction stage. The training stage comprises the following steps: preprocessing RNA data, encoding the RNA data, constructing a neural network and training network parameters. A mathematically abstract statistical mode of RNA is converted into a matrix form, the matrix form is input into a pre-designed attention mechanism-based multi-scale convolutional neural network, and parameters in the neural network are trained by using an Adam optimization method by minimizing a designed special cross entropy loss function. In the prediction stage, RNA sequence data with four basic groups as basicunits are input into the network, the probability of whether binding sites corresponding to binding proteins exist in the RNA data is output by the last layer of the neural network, and therefore theprediction result of the RNA sequence category is obtained. The method can improve the prediction precision.

Description

Technical field [0001] The invention relates to the field of biological information technology, and specifically relates to a method and device for predicting RNA-binding proteins based on multi-scale attention convolutional neural networks. Background technique [0002] Bioinformatics is a technology that uses mathematical models, statistical methods and computers to process biological data. Bioinformatics is a new interdisciplinary discipline that emerged with the launch of the Human Genome Project. In bioinformatics, the study of DNA / RNA and proteins is particularly important. DNA / RNA is the carrier and transmitter of genetic information in organisms and participates in important biochemical processes such as the transcription and translation of genetic information. Proteins are the basis of life. The material basis, this organic macromolecule, is the basic organic matter that constitutes cells and is the main carrier of life activities. Studying DNA / RNA and proteins is ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B5/20G16B15/20G16B20/30G16B40/00G06N3/04
CPCG16B5/20G16B40/00G16B20/30G16B15/20G06N3/045
Inventor 杜博刘子翼罗甫林
Owner WUHAN UNIV
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