A distance measurement method for gene expression profiles based on deep learning

Abstract

The invention belongs to the field of gene expression spectrum classification, discloses a gene expression spectrum distance measurement method based on deep learning, and belongs to the mining and application of deep learning on biological big data. First, a convolutional neural network model suitable for gene feature metric learning is designed to extract the characteristics of the data, and then the distance between the data is calculated by using the improved cosine distance, and finally the classification effect of the classification algorithm is used to measure the performance of the method excellent. This method can quickly and efficiently measure the similarity between different gene expression profiles, and provide data for subsequent studies such as gene classification, clustering, differential expression analysis, and compound screening. Compared with the traditional gene enrichment method, this method significantly improves the distance measurement effect between data, and can effectively reduce the manual intervention in gene expression profile analysis, avoiding the over-fitting phenomenon that is easy to occur in conventional deep networks. This method has strong transferability.

Description

Technical field: [0001] 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. Background technique: [0002] 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 expression profil...

AI Technical Summary

Problems solved by technology

[0003] 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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