Method for comprehensively analyzing gene sub-graph similarity probability current by use of multiple image detection technologies

A comprehensive analysis and image detection technology, applied in sequence analysis, special data processing applications, instruments, etc., to achieve the effect of predicting disease risk

Active Publication Date: 2017-01-04
广州麦仑信息科技有限公司
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[0007] In various image processing retrieval and rec

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  • Method for comprehensively analyzing gene sub-graph similarity probability current by use of multiple image detection technologies
  • Method for comprehensively analyzing gene sub-graph similarity probability current by use of multiple image detection technologies
  • Method for comprehensively analyzing gene sub-graph similarity probability current by use of multiple image detection technologies

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Embodiment Construction

[0029] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the schematic embodiments and descriptions are only used to explain the present invention, but are not intended to limit the present invention.

[0030] Such as Figure 1-Figure 4 As shown, the method for comprehensively analyzing the similarity probability of gene subgraphs using multiple image detection techniques described in this specific embodiment adopts the following method steps:

[0031] A. Data preparation for the full human gene sequence map and target gene submap;

[0032] B. Using CNN convolutional neural network to detect the similarity probability of gene subgraphs;

[0033] C. Using HOG+SVM classification to detect the similarity probability of gene subgraphs;

[0034] D. Use the Adaboost+LBP feature algorithm to detect the similarity probability of gene subgraphs;

[0035] E, using the standard correlation coefficien...

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Abstract

The invention relates to the technical field of image detection and processing, simultaneously relates to the field of bioinformatics, and in particular relates to a method for comprehensively analyzing gene sub-graph similarity probability current by use of multiple image detection technologies. The method comprises the following steps: A, data preparation of a human body gene sequence total graph and a target gene sub-graph; B, detecting the gene sub-graph similarity probability current by use of a CNN (Convolutional Neural Network); C, detecting the gene sub-graph similarity probability current by use of HOG+SVM classification; D, detecting the gene sub-graph similarity probability current by use of Adaboost+LBP feature algorithm; E, detecting the gene sub-graph similarity probability current by use of a standard correlation coefficient template matching method; F, comprehensively analyzing the probability current respectively obtained in the step B, step C, step D and step E by use of a BP neural network classifier to obtain the final probability current after the weighted summation. The method disclosed by the invention can be applied to disease gene detection and capable of fast and accurately detecting whether the human body gene sequence contains the disease susceptibility gene and predicting the disease risk of the body.

Description

【Technical field】 [0001] The invention relates to the technical field of image detection and processing, and at the same time relates to the field of bioinformatics, in particular to a method for comprehensively analyzing the similarity probability of gene subgraphs by using multiple image detection technologies. 【Background technique】 [0002] There are four main types of image detection technologies: CNN convolutional neural network detection algorithm, HOG+SVM classification detection, Boost classification+LBP feature algorithm, and standard correlation coefficient template matching method. [0003] CNN (Convolutional Neural Networks) convolutional neural network algorithm is a kind of artificial neural network, which has become a research hotspot in the field of speech analysis and image recognition. Its advantages are more obvious when the input of the network is a multi-dimensional image, making the image It can be directly used as the input of the network, avoiding th...

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Application Information

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IPC IPC(8): G06F19/00G06K9/62
CPCG16B30/00G06F18/285G06F18/254
Inventor 余孟春何庆瑜特伦斯·古力谢清禄朱军王一为
Owner 广州麦仑信息科技有限公司
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