计算式主机范围预测-好,坏和丑的
Abigail A Howell, Cyril J Versoza1, Susanne P Pfeifer1
1Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
Virus evolution
|February 16, 2024
概括
预测细菌宿主范围的计算工具显示出有希望的结果,但需要改进. 目前的方法在菌株级准确性方面存在困难,这限制了它们在医学和农业等领域的直接应用.
科学领域:
- 微生物学 微生物学
- 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:
- 抗微生物耐药性需要替代治疗方法,引发对细菌菌的兴趣.
- 在医学,农业和生物技术中,菌体的应用需要准确的宿主范围数据.
- 对菌体宿主范围的实验性确定是耗时和劳动密集的.
研究的目的:
- 为了对计算工具的性能进行基准测试,用于预测菌体宿主范围.
- 评估机器学习和深度学习方法对宿主预测的准确性和精度.
- 为了确定当前在实践中菌体应用的形方法的局限性.
主要方法:
- 利用了16种具有实验验证实宿主范围的宽谱细菌菌体.
- 评估了11个最近开发的计算主机范围预测工具.
- 评估预测准确度,精度和灵敏度在物种,属和菌株级别.
主要成果:
- 机器学习和深度学习模型在物种/属级别上表现出高准确度和精度.
- 应变水平预测显示中度灵敏度 (<80%),但精度低 (<40%).
- 目前的计算工具更适合于元基因组学而不是特定菌株向.
结论:
- 在 silico 宿主范围预测是一个有价值但正在发展的领域.
- 计算工具需要进一步改进,以有效指导实验性菌体选择.
- 增强的菌株水平预测准确性对于细菌菌体的实际应用至关重要.
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