Visualization for Viral Genome Space
组织者
演讲者
时间
2023年11月17日 21:00 至 21:30
地点
Online
摘要
Understanding the relationships between genomes plays a crucial role in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families. We formulate the selection of optimal weights for the k-mer natural vector method as an optimization problem. Experimental results demonstrate that the utilization of the optimal weight significantly enhances classification accuracy, achieving 92.73% on the testing set. Moreover, the optimal metric enables the visualization for viral genome space, providing valuable insights into genome relationships from a mathematical perspective.