用机器学习算法预测宿主-病原体相互作用:一个范围审查
Rasool Sahragard1, Masoud Arabfard2, Ali Najafi1
1Molecular Biology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
概括
机器学习有效地预测宿主-病原体相互作用 (HPI),以树为基础的算法是最常见的. 在数据集标准化和可解释性方面仍然存在挑战,以推动在病原体研究中推进AI.
科学领域:
- 微生物学和免疫学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 病原性微生物对全球健康构成挑战,需要了解宿主-病原体相互作用 (HPI).
- 蛋白与蛋白相互作用 (PPI) 是HPI的关键,对治疗开发至关重要.
- 对HPI的实验方法是劳动密集型的;人工智能和机器学习提供高效的预测.
研究的目的:
- 系统地审查和评估用于预测宿主-病原体相互作用 (HPI) 的机器学习方法.
- 根据宿主/病原体类型,算法和评估指标对现有研究进行分类.
- 确定挑战,并为人工智能驱动的HPI预测未来研究提供路线图.
主要方法:
- 2019-2024年基于机器学习的HPI预测研究的范围审查.
- 使用与HPI相关的关键字搜索了知名的数据库.
- 根据标题和摘要评估,在46篇相关文章中选择了30篇.
主要成果:
- 基于树的算法 (随机森林,渐变增强) 在HPI预测中最为普遍.
- 深度学习模型 (CNN,RNN) 是有前途的,但需要大量的标记数据.
- 在数据集标准化和模型可解释性方面存在重大差距.
结论:
- 机器学习对HPI预测具有重大潜力.
- 解决数据集质量,特征选择和模型透明度方面的挑战至关重要.
- 本综述提供了计算方法的系统比较,指导未来的AI驱动病原体研究.
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