Gene expression data classification method and system

Abstract

The invention discloses a gene expression data classification method and system. The gene expression data classification method comprises the following steps: after obtaining a gene characteristic data set, clustering the gene characteristic data set by adopting a clustering algorithm to obtain a first pre-set parameter clustering set; and processing the clustering set to obtain a second sample matrix, a second training set and a characteristic index set, and reducing dimensions of gene characteristic data so as to reduce the redundancy of the gene characteristic data, and reduce occupied computational resources and consumed computation time in a process of carrying out characteristic selection on the second training set to the very great extent. The occupied computational resources and the consumed computation time in a process of carrying out clustering operation on the gene characteristic data set by adopting the clustering algorithm are less, so that the occupied computational resources and the consumed computation time in a process of clustering gene characteristic data to be detected through adopting the gene characteristic data classification method are less.

Description

technical field [0001] The present application relates to the technical field of gene classification, and more specifically, to a method and system for classifying gene expression data. Background technique [0002] Using DNA microarray counting can simultaneously measure tens of thousands of dimensional gene expression data, and these expression data can help researchers study the nature of organisms. However, in a large amount of gene expression data, only a small amount of gene expression data is the research object of researchers. Taking the study of cancer genes as an example, the sample of cancer gene expression data is usually less than one hundred, while in a large number of gene expression data Sorting cancer genes from other genes in the data requires a lot of computing resources and computing time. [0003] Some researchers use the support vector machine recursive feature elimination (SupportVectorMachineRecursiveFeatureElimination, SVM-RFE) algorithm to automati...

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Problems solved by technology

[0005] In order to solve the above technical problems, the present invention provides a gene expression data classification method and a classification

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