Gene selection method and device

Abstract

The invention provides a gene selection method which is used for gene characteristic selection. The method comprises the steps of obtaining a training set and a test set through a gene data microarraydata set, and determining an initialized population; carrying out binary coding on each individual of the current population by adopting a conversion function; calculating the fitness value of the current population, and updating related parameters in the dolio-sea squirt and moth fire suppression strategy; setting related parameters of a sine and cosine optimization algorithm, and updating the population by adopting a sine and cosine optimization algorithm iterative formula; updating the populations obtained through the sine and cosine optimization algorithm through a doliola scabra, moth fire suppression and reverse learning strategy in sequence so as to obtain three populations; selecting a next generation of population through greedy selection; and if the maximum number of iterationsis reached, ending the loop and outputting the optimal solution, otherwise, continuing the iteration until the iterative computation is ended. According to the invention, the gene characteristics which contribute most to categories can be screened out from the genes more accurately and more efficiently, and the detection cost is reduced.

Description

technical field [0001] The invention relates to a gene selection technology in the field of data preprocessing, in particular to a gene selection method. Background technique [0002] In today's competitive world where human beings are affected by various diseases especially cancer, leukemia, etc., medical tests help to determine the symptoms and causes of various diseases. With the rapid development of related technologies in the field of biomedicine and health, a large amount of bioinformatics and clinical medical data, especially molecular biology experiment data and genetic data, are growing at an unprecedented rate. Although humans have studied the occurrence and development of various diseases at the molecular level and found a large number of disease-causing genes, people still have little understanding of their occurrence and regulation mechanisms. The analysis of microarray gene expression data and protein expression data can be used to grasp the information of phy...

Images