Aiming at the drawbacks of low efficiency and long time consumption in sequence deletion analysis of high-throughput sequencing biological instruments after obtaining gene sequence data.The study applies support vector machine for feature extraction of missing sequence data,and then improves the support vector machine using particle swarm optimization algorithm to construct a new gene deletion analysis model.The results showed that the average data processing accuracy and data missing false alarm rate of the gene sequence analysis model were 96.81%and 1.85%,respectively.The average data calculation efficiency and F1 value of the gene sequence analysis model are 95.81%and 0.91,respectively.This indicates that the gene sequence analysis model constructed in the study not only has high computational efficiency when processing gene deletion data,but also can effectively improve the perform-ance of gene deletion data classification,achieve efficient and accurate detection of gene deletions,and provide new analytical tools for research in the fields of bioinformatics and genomics.
关键词
支持向量机/粒子群优化算法/高通量测序/生物仪器/数据分析
Key words
support vector machine/particle swarm optimization algorithm/high throughput sequencing/biological instruments/data analysis
维普
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