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Genome unit point variation pathogenicity prediction method and system and storage medium

A prediction method and a single-point technology, applied in the field of bioinformatics, can solve the problems of high cost, low prediction accuracy, and low reliability, and achieve the effects of cost saving, convenient use of symptoms, and protection of patient privacy

Active Publication Date: 2019-09-17
TSINGHUA UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method, system and storage medium for predicting the pathogenicity of a genome single-site variation, so as to solve the problem of low accuracy, low reliability, and low cost of predicting the pathogenicity of a genome single-site variation in the prior art. expensive problem

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  • Genome unit point variation pathogenicity prediction method and system and storage medium
  • Genome unit point variation pathogenicity prediction method and system and storage medium
  • Genome unit point variation pathogenicity prediction method and system and storage medium

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Embodiment Construction

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention and the accompanying drawings in the embodiments:

[0031] The method for predicting the pathogenicity of genome single-site mutations provided by the present invention is based on deep learning methods, using genome single-site mutation data obtained by sequencing and reference genome sequences (also known as evolutionary conservation data) of various species as training data, and using The medical diagnosis result is used as a category label, and the model obtained after training can calculate the data of various single-point variations and predict the probability of causing genetic diseases according to different needs.

[0032] The model basis of the present invention is a hybrid of convolutional neural networks and multi-layer perceptrons. The purpose of the convolutional neural network i...

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Abstract

The invention relates to the technical field of bioinformatics, and provides a genome unit point variation pathogenicity prediction method and system and a storage medium. The method comprises the following steps: obtaining genome unit point variation data and coevolution conservative data according to a genome unit point variation position and a variation condition; preprocessing the genome unit point variation data and the coevolution conservative data to generate a matrix; loading the model, inputting the matrix, performing feature extraction through the densely connected convolutional neural network, splicing the feature data by adopting the multilayer perceptron, performing calculation, and outputting a prediction result. By adopting the method, the problems of low prediction accuracy, low reliability and high cost of genomic unit point variation pathogenicity in the prior art can be solved.

Description

technical field [0001] The invention relates to the technical field of bioinformatics, in particular to a method, system and storage medium for predicting the pathogenicity of a genome single point variation. Background technique [0002] With the development of biomedical technology, the quality of high-throughput sequencing has been continuously improved and the cost has been continuously reduced, making genome sequencing more and more widely used in human precision medicine, especially the diagnosis and treatment of potential disease risks of subjects through sequencing. Screening is a research hotspot in precision medicine. At the same time, the rapid development of machine learning methods in recent years has led people to try to use machine learning to solve some problems in the medical field. However, limited by the scarcity of medical diagnosis data and people's insufficient understanding of the characteristics of genome sequences, there has been a lack of a high-ac...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N20/00G16B20/30G16B30/10G16B40/00G16B50/30
CPCG16B30/10G16B40/00G16B50/30G06N20/00G16B20/30G06N3/045G06F18/214
Inventor 江瑞宋绍铭
Owner TSINGHUA UNIV
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