Application of K-means clustering based on artificial intelligence in gene statistics of biological information engineering

In the field of bioinformatics, the algorithm of biological gene sequence has always been one of the hot problems in scientific research. With the combination of biology and artificial intelligence technology, genetic algorithm data is increasing. At the same time, the emergence of a new generation of sequencing technology, the decrease in sequencing time and cost, and the high sequencing throughput have significantly increased the sequence data, showing an exponential growth trend, and there are still new biological gene sequence data found and recorded every day, and the speed of data generation is much faster than the speed of data processing, so the processing of large-scale DNA sequencing data needs more efficient methods. Therefore, AI bioinformatics engineering, especially genetic algorithms and K-means cluster analysis, has become an important tool in the field of bioinformatics statistics, with particular impact on the detection and diagnosis of single nucleotide polymorphisms (SNPS) associated with contact dermatitis. These advanced AI models efficiently process large amounts of genomic data, including SNP datasets, and have the ability to analyze patient genotype information. By doing so, they can identify SNPS that are strongly associated with contact dermatitis and establish meaningful associations between these genetic variants and the disease.The adoption of this personalized medicine approach not only addresses the specific needs of individual patients but also significantly enhances the success rate of treatment. Through the analysis of convolution algorithm and gene sequence in biological information engineering, this paper focuses on the relevant experiments of gene K-cluster analysis model, demonstrating the advantages and reference significance of gene algorithm under K-cluster analysis for current gene information statistics.