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A Convolutional Neural Network Based Nucleosome Classification Prediction Method

A convolutional neural network, classification prediction technology, applied in the field of genetic classification prediction, can solve problems such as limited positioning accuracy

Active Publication Date: 2021-06-11
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

Based on a method called "iNuc-PseKNC" (Guo S H, Deng E Z, Xu L Q, et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudok-tuple nucleotide composition. [J]. Bioinformatics ,2014,30(11):1522) is the core algorithm for predicting nucleosome position, but most of the existing prediction algorithms are only based on the statistical characteristics of nucleosomes, and the positioning accuracy is very limited.

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  • A Convolutional Neural Network Based Nucleosome Classification Prediction Method
  • A Convolutional Neural Network Based Nucleosome Classification Prediction Method
  • A Convolutional Neural Network Based Nucleosome Classification Prediction Method

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[0042] The content of the present invention will be further described below in conjunction with the accompanying drawings and embodiments, but the present invention is not limited.

[0043] Example:

[0044] refer to figure 1 , a method for predicting nucleosome classification based on a convolutional neural network, comprising the following steps:

[0045] 1) Feature extraction: select the DNA sequences of the nucleosomes or linkers of Homo sapiens, nematodes and Drosophila melanogaster in the UCSC genome database. Refers to the base pair, which converts the 16 combinations of the dinucleotide ATCG in the DNA sequence of each nucleosome or linker into a 16-dimensional vector through one-hot encoding. The feature vector is expressed as formula (1) :

[0046] x i =(P i,1 ,P i,2 ,...,P i,16 ) T (1)

[0047] x i Indicates the eigenvector of the i-th nucleosome or link body at this time, P i,1 ,P i,2 ,...,P i,16 Represents one-hot encoding of 16 combinations of dinuc...

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Abstract

The invention discloses a method for classifying and predicting nucleosomes based on a convolutional neural network, which is characterized in that it comprises the following steps: 1) feature extraction; 2) extracting the physical and chemical properties of nucleotides in nucleosome or linker DNA sequences ;3) Adding biological characteristics; 4) Obtaining the 24th dimension vector; 5) Adding chemical properties of nucleotides; 6) Obtaining a matrix containing biological information; 7) Constructing a convolutional neural network structure; 8) Classifying nucleosomes. This method can accurately predict the classification of nucleosomes.

Description

technical field [0001] The invention relates to classification prediction of genetics, in particular to a method for prediction of nucleosome classification based on convolutional neural network. Background technique [0002] Nucleosome prediction is an important part of current genetic research. The special structure of nucleosomes limits the contact between proteins responsible for basic life processes and DNA surrounding histones, so its formation and precise positioning on chromatin are important in genes. It plays an irreplaceable role in the expression process, directly or indirectly affecting basic biological processes such as transcription. Nucleosome positioning is an important way to regulate gene transcription in eukaryotes. To thoroughly understand the regulatory information of gene expression, it is necessary to consider the regulatory role of nucleosome positioning. The relationship between nucleosome position information and gene expression regulation is curre...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B40/00G06N3/04
CPCY04S10/50
Inventor 樊永显龚浩蔡国永张向文张龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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