Gene expression profile distance measurement method based on deep learning

Abstract

The invention belongs to the field of gene expression profile classification, discloses a gene expression profile distance measurement method based on deep learning, and belongs to mining and application of deep learning on biological big data. Firstly, a convolutional neural network model suitable for gene characteristic metric learning is designed to extract data characteristics, then the distance between the data is calculated by applying an improved cosine distance, and finally, the good performance of the method is measured through the classification effect of a classification algorithm.According to the method, the similarity between different gene expression profiles can be quickly and efficiently measured, and data is provided for subsequent researches such as gene classification,clustering, differential expression analysis and compound screening. Compared with a traditional gene enrichment method, the method has the advantages that the distance measurement effect between thedata is obviously improved, the manual intervention during gene expression profile analysis can be effectively reduced, the overfitting phenomenon easily generated by a conventional deep network is avoided, and the method has relatively high mobility.

Description

[0001] Technical field: [0002] The present invention belongs to the field of gene expression spectrum classification, and more specifically relates to the mining and application of deep learning on gene expression spectrum data, and in particular to a method for measuring the distance of gene expression spectrum based on deep learning. [0003] Background technique: [0004] At present, with the rapid development of biotechnology, the experimental methods and research methods in the field of biomedicine have undergone tremendous changes, showing the trend of "big data". Among them, the similarity comparison of expression profile data can be applied to compare the expression levels of genes in normal and abnormal cells, help identify disease-related genes and drug targets, and analyze the pathogenic mechanism of complex diseases. Therefore, the similarity of gene expression profiles Research has gradually become a research hotspot. At present, the calculation method of gene ex...

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Problems solved by technology

[0006] The technical problem to be solved in the present invention is to give full play to the advantages of deep metric learning that can accurately obtain the characteristics of data and can quickly and effectively calculate the distance between data, so as to solve the problem of poor performance and time overhead in calculating gene expression spectrum distance in traditional methods. big problem

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