Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Recurrent Ebola outbreaks: reframing spillover from pathogen-centred narratives to ecological disruption, structural vulnerability, and proactive preparedness.

The Lancet. Microbe·2026
Same author

Evolutionary dynamics and molecular adaptation of Rift Valley fever virus across human and non-human outbreaks in Africa.

BMC genomics·2026
Same author

Ebola outbreak caused by Bundibugyo virus: challenges and priorities for epidemic preparedness and response.

Lancet (London, England)·2026
Same author

Global inequities in hepatitis B and C genomic surveillance revealed through an interactive data integration dashboard.

Public health·2026
Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026
Same author

Impact of climate-induced human migration on dengue exposure risk in Africa.

Research square·2026

相关实验视频

Updated: May 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K

工艺:一种机器学习方法来确定登革热的亚型.

Daniel J van Zyl1,2, Marcel Dunaiski2, Houriiyah Tegally1

  • 1Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.

bioRxiv : the preprint server for biology
|February 24, 2025
PubMed
概括

一个新的机器学习框架,Craft (混乱随机森林),提供快速而准确的登革热病毒亚型. 该工具显著改进了追踪病毒演变和疾病监测的现有方法.

更多相关视频

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

6.5K
Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

14.2K

相关实验视频

Last Updated: May 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.3K
A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

6.5K
Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

14.2K

科学领域:

  • 病毒学 病毒学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 登革热病毒每年导致近3.90亿例感染,需要有效跟踪其演变.
  • 一个分层的命名系统增强了登革热病毒亚型的空间分辨率.
  • 当前的子类型化工具在计算上非常密集,限制了快速分类.

研究的目的:

  • 介绍Craft (混沌随机森林),用于登革热病毒亚型的机器学习框架.
  • 与现有的登革热亚型工具相比,评估Craft的速度和准确性.

主要方法:

  • 开发了一个名为Craft (混沌随机森林) 的新型机器学习框架.
  • 与基因组侦探,GLUE和NextClade等既有工具进行基准测试.
  • 测试Craft在保留数据集上的准确性及其在短序段的性能.

主要成果:

  • 工艺品在持久测试套件上达到99.5%的准确性.
  • 机器每分钟处理超过14万个序列,显示出卓越的速度.
  • 高精度保持,即使序列段短至700个核酸.

结论:

  • 工艺为登革热病毒亚型化提供了更快,更准确的替代方案.
  • 该框架有助于有效跟踪病毒演变和公共卫生监测.
  • 机器学习为快速基因组流行病学提供了一个有希望的方法.