A Methodology for Modeling Multiple Sequence Classification of Biological Genes Based on Improved Genetic Clustering
ZHONG Pingting
XU Shengchao
Abstract：A multi-sequence classification model of biological genes based on improved genetic clustering is investigated to provide basic data for genetic engineering.A hidden Markov model(HMM)is used to model each gene sequence.The mechanism of selection,crossover and variation of the genetic algorithm is improved by using the quantum optimization algorithm.The improved genetic algorithm is used to search the cluster center.By dynamically adjusting the quantum rotation angle,the adaptive mutation operator is introduced to enhance the global search ability of the optimal cluster center.After the improved genetic algorithm is used to optimize the cluster center of the K-means clustering algorithm.The accurate classification of multiple gene sequences is achieved.The experimental results show that the method can achieve biological gene multi sequence classification,the samples with-in the class are densely distributed,and the samples between classes have a high degree of discrimination,with the average F1 score index reaching 93.45%.
Keywords：gene sequencehidden Markov modelstate transition matrixK-means clusteringcluster centerquantum ge-netic algorithm
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 3512-3516,3522 )