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

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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