Feature gene selection method based on deep learning and evolutionary computation

An eigengene, deep learning technology, applied in the field of bioinformatics, can solve problems such as lack of interpretability

Active Publication Date: 2018-05-04
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, these hybrid methods lack interpretability while simp

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  • Feature gene selection method based on deep learning and evolutionary computation
  • Feature gene selection method based on deep learning and evolutionary computation
  • Feature gene selection method based on deep learning and evolutionary computation

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Embodiment Construction

[0069] The present invention will be further described below in conjunction with the accompanying drawings.

[0070] refer to Figure 1 ~ Figure 3 , a feature gene selection method based on deep learning and evolutionary computation, comprising the following steps:

[0071] 1) Select differentially expressed genes and establish a first-level gene pool. The process is as follows:

[0072] 1.1) Calculate the differential expression level index of each gene in the original gene pool, that is, the IIC-FC index:

[0073]

[0074] Equation (1) is suitable for the calculation of gene differential expression level in multi-classification data sets, where c represents the number of genes in the original gene pool, and represent the average expression levels of gene i and gene j, respectively, and represent the standard deviation of the expression levels of gene i and gene j respectively, the functions max{·,·} and min{·,·} represent the maximum value and minimum value respe...

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Abstract

A feature gene search method based on deep learning and evolutionary computation includes the following steps: 1) calculating a differential expression level index and establishing a primary gene poolaccording to a Pareto principle; 2) according to the expression level of each gene, calculating a density matrix and a distance matrix after the mapping of the genes, drawing a decision map, using multiple linear regression analysis to fit a binary plane, and automatically determining a clustering center; 3) constructing a deep gene expression prediction network to calculate gene-genetic susceptibility information (GGSI) of a primary gene pool; 4) removing redundant genes based on GGSI values and creating a secondary gene pool; 5) performing binary coding on a cuckoo search algorithm based onthe GGSI values, selecting the most compact gene sets, and establishing a tertiary gene pool. According to the method, a feature gene selection framework is provided based on a hierarchical structure, which can extract key genes better and use multiple linear regression analysis combined with a deep learning algorithm and an optimization algorithm to select the most compact feature gene sets.

Description

technical field [0001] The invention belongs to the field of biological information, and in particular relates to a method for selecting characteristic genes. Background technique [0002] With the advancement of gene sequencing technology, high-throughput sequencing technology provides a large amount of gene expression data. Gene expression profiling is widely used as a tool for capturing cellular expression patterns following disease occurrence, genetic perturbation, and drug treatment. Facing a large amount of high-dimensional gene expression data, how to mine useful information has become the research focus in the field of bioinformatics. [0003] Due to the high-dimensional nature of gene expression data, mining characteristic genes with key information is of great significance for subsequent gene data processing and sample phenotype analysis. At present, some researchers combine binary particle swarm optimization algorithm (BPSO) and filtering method to find the best...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04G06K9/62
CPCG06N3/086G06N3/045G06F18/23G06F18/214
Inventor 陈晋音郑海斌刘靓颖宣琦应时彦李南施朝霞
Owner ZHEJIANG UNIV OF TECH
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