Gene characteristic extraction method based on manifold learning and closed loop deep convolutional dual-network model

Abstract

The invention discloses a gene characteristic extraction method based on manifold learning and a closed loop deep convolutional dual-network model. The method comprises the following steps: 1, coarse extraction of a cancer-related gene characteristic based on manifold learning; 2, fine extraction of a gene characteristic based on a closed loop deep convolutional dual-network structure, wherein the process of the fine extraction is as follows: by adopting a dual-network structure consisting of a forward convolutional neural network and a backward convolutional neural network, performing deep abstraction on gene expression data through the characteristic extraction capacity of the convolutional neural networks, and finally projecting key characteristics, wherein the backward convolutional neural network realizes inverse projection of the key characteristics. The invention provides the gene characteristic extraction method based on manifold learning and the closed loop deep convolutional dual-network model, which can retain the gene characteristic to the maximum extent and realize fast dimension reduction.

Description

technical field [0001] The invention relates to the technical field of gene feature extraction, in particular to a gene feature extraction method. Background technique [0002] The era of precision medicine has gradually arrived, and the accurate diagnosis and precise treatment of cancer bear the brunt. In China, 6 people are diagnosed with malignant tumors every minute, and the lifetime probability of Chinese residents suffering from cancer is 22%. Cancer has become the leading cause of death for Chinese residents. Cancer prevention and treatment are the focus of scientists in various disciplines. With the reduction of the cost of gene sequencing, by sequencing and comparing the gene expression data of normal people and cancer patients, a cancer risk assessment report can be obtained, which is also a relatively advanced means of early detection of cancer. At the same time, it also analyzes the progress and effect of treatment by tracking and detecting gene expression data ...

AI Technical Summary

Problems solved by technology

[0009] In order to overcome the shortcomings of the existing gene feature extraction methods, which are slow in dimensionality reduction and unable to preserve gene features to the greatest extent, the present invention provides a

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