Gene Sequence Analysis of COVID-19 Based on Dimension Reduction Combination and Clustering
WANG Zhenglin
ZHENG Qianying
CHEN Jiansen
ZHENG Qiao
Abstract：In order to deal with the COVID-19 that is still raging,many researchers analyze the gene sequence of COV-ID-19,and cluster analysis is an effective means of analysis.This paper analyzes the gene sequence data of COVID-19 with cluster-ing algorithm as the core,and uses a combination of stepwise dimension reduction and clustering to solve the problem that high-di-mensional gene sequence mutation data is not suitable for direct clustering.By using two common datasets to train several commonly used algorithms after dimension reduction and clustering combination,the best combination of PCA+UMAP dimension reduction and Birch clustering algorithm is selected.Finally,the mutation data of COVID-19 gene sequence is input into the algorithm model for classification.At the same time,further analysis also confirmed that the mutation frequency of S protein of COVID-19 is very high,and S protein is closely related to the high infectivity of the virus,which is consistent with the conclusions reached by relevant researchers.
Keywords：novel coronavirusdimension reduction algorithmclustering algorithmalgorithm combination
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3174-3178,3219 )