Cancer gene expression profile data identification method based on integration of extreme learning machines

Abstract

The invention discloses a cancer gene expression profile data identification method based on integration of extreme learning machines. The method includes the steps of selection and integration of member extreme learning machines (ELMs). The method concretely includes the steps that preprocessing is carried out on a cancer gene expression profile data set, wherein the preprocessing includes gene selection and normalization of expression profile data; N sample sets are generated through a Bagging method, and each sample set is divided into a training set and a verification set according to a certain proportion; N ELMs are generated on the N training sets in a learning mode, and L ELMs (L&1t; N) with the highest recognition rate on the corresponding verification sets are selected to form an alternative member ELM base; K member ELMs (K&1t; L) forming an integrated system are selected from L ELMs based on the particle swarm optimization algorithm; the integrated vote weight of K member ELMs is worked out by utilizing the minimum-norm least square method; an integrated ELM system is obtained, and the integrated ELM system is used for performing tumor recognition on a newly increased cancer gene expression profile sample. Through the method, the cancer gene expression profile data can be quickly and accurately recognized.

Description

technical field [0001] The invention belongs to the application field of computer analysis technology of tumor gene expression profile data, and in particular relates to a method for identifying tumor gene expression profile data based on an integrated extreme learning machine. Background technique [0002] In life science research, while DNA microarray technology brings unprecedented opportunities for biological and medical research, the complex gene expression profile data it generates poses great challenges to existing data analysis and processing methods. First, gene expression profile data has very high dimensions (genes), and there are very complex relationships between these gene dimensions. Second, gene expression profile data have fewer samples, which contradicts the huge number of genes that constitute an imbalance. Third, gene expression profile data inherently have data analysis difficulties such as high noise and high variation. Fourth, a large amount of usefu...

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Problems solved by technology

Second, gene expression profile data has fewer samples, which contradicts the huge number of genes that constitute an imbalance

Third, gene expression profile data inherently have data analysis difficulties such as high noise and high variation

However, these integrated ELMs simply integrate multiple ELMs, and the selection of member ELMs and the design of integration rules are too simple, so there is still a lot of room for improvement in their performance

So far, there is no corresponding report on the integration of ELM for gene expression profile data for tumor identification

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