Chen Di, Xu Lixia, Xing Huiwu, Shen Weitao, Song Ziguang, Li Hongjiang, Zhu Xuqiang, Li Xueyuan, Wu Lixin, Jiao Henan, Li Shuang, Yan Jing, He Yuting, Yan Dongming
Department of Neurosurgery The First Affiliated Hospital of Zhengzhou University, Zhengzhou University Henan China.
Department of Infectious Diseases The First Affiliated Hospital of Zhengzhou University, Zhengzhou University Zhengzhou China.
Imeta. 2024 Sep 2;3(5):e238. doi: 10.1002/imt2.238. eCollection 2024 Oct.
In recent years, development in high-throughput sequencing technologies has experienced an increasing application of statistics, pattern recognition, and machine learning in bioinformatics analyses. SangeBox platform to meet different scientific demands. The new version of Sangs is a widely used tool among many researchers, which encourages us to continuously improve the plerBox 2 (http://vip.sangerbox.com) and extends and optimizes the functions of interactive graphics and analysis of clinical bioinformatics data. We introduced novel analytical tools such as random forests and support vector machines, as well as corresponding plotting functions. At the same time, we also optimized the performance of the platform and fixed known problems to allow users to perform data analyses more quickly and efficiently. SangerBox 2 improved the speed of analysis, reduced resource required for computer performance, and provided more analysis methods, greatly promoting the research efficiency.
近年来,高通量测序技术的发展使统计学、模式识别和机器学习在生物信息学分析中的应用日益增加。SangeBox平台可满足不同的科学需求。新版Sangs是众多研究人员广泛使用的工具,这促使我们不断改进SangerBox 2(http://vip.sangerbox.com),并扩展和优化交互式图形功能以及临床生物信息学数据的分析功能。我们引入了随机森林和支持向量机等新型分析工具以及相应的绘图功能。同时,我们还优化了平台性能并修复了已知问题,以便用户能够更快速高效地进行数据分析。SangerBox 2提高了分析速度,降低了计算机性能所需的资源,并提供了更多分析方法,极大地提升了研究效率。