使用机器学习技术和下一代测序进行传染病诊断的广泛审查:最先进的技术和前景
Javed Aalam1, Syed Naseer Ahmad Shah1, Rafat Parveen1
1Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India.
Computers in biology and medicine
|March 7, 2025
概括
将机器学习 (ML) 与下一代测序 (NGS) 集成,为COVID-19等传染病提供高度准确的诊断. 这种强大的组合增强了疾病的检测和管理,改善了全球健康结果.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 传染病 (结核病,艾滋病毒/艾滋病,COVID-19) 给全球健康带来了重大挑战.
- 下一代测序 (NGS) 和机器学习 (ML) 是疾病诊断和管理的关键技术.
研究的目的:
- 审查ML技术与NGS在传染病诊断中的整合.
- 突出这种整合的有效性,并确定整合的挑战.
主要方法:
- 在主要的科学数据库中进行了长达十年的文献审查.
- 分析了研究论文,文章和会议记录,这些论文是关于对NGS和元基因组NGS (mNGS) 数据应用的ML算法.
主要成果:
- 机器学习算法 (DNN,SVM,KNN) 在诊断传染病方面达到95%以上的准确性.
- 深度学习在基因组/元基因组分析方面表现出色;传统算法也表现出强大的性能.
- 挑战包括数据类型依赖和病原体差异化.
结论:
- ML和NGS的整合显著推进了传染病诊断,以快速,精确的检测.
- 解决目前的局限性将进一步提高技术的有效性和全球公共卫生.
- 这种方法有望改善传染病管理的结果.
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