主机-微生物相互作用:机器学习和深度学习技术在动物病毒性疾病管理中的前景
Yiting Lu1,2,3, Xiaowen Li1,2,3, A M Abd El-Aty4,5
1Marine Medical Research and Development Centre, Shenzhen Institute, Guangdong Ocean University, Shenzhen 518120, China.
Veterinary sciences
|December 31, 2025
概括
机器学习 (ML) 和深度学习 (DL) 通过预测宿主微生物相互作用和加速药物发现,为打击动物病毒性疾病提供了强大的工具. 未来的进步旨在适应性系统,整合病毒学,免疫学和流行病学,以精确预防疾病.
科学领域:
- 兽医科学 兽医科学 兽医科学
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 工业化畜牧业生产增加了病毒流行病的风险.
- 了解病毒与宿主之间的相互作用对于病原体控制至关重要.
- 动物病毒性疾病会影响肠道,呼吸道和生殖系统.
研究的目的:
- 审查机器学习 (ML) 和深度学习 (DL) 在动物病毒性疾病研究中的应用.
- 突出ML/DL如何改善兽医科学的预测,监测和发现.
- 为了确定AI在动物病毒学中的挑战和未来方向.
主要方法:
- 对动物病毒性疾病中的ML/DL应用现有研究的审查.
- 分析这些技术如何模拟生物过程并帮助兽医科学.
- 识别诸如数据不平衡和模型可转移性等挑战.
主要成果:
- ML和DL增强了对宿主微生物相互作用的预测.
- 这些技术支持持续的动物健康监测.
- 能够加速发现药物标和疫苗候选人.
- 通过集成的ML/DL框架,可以实现复杂生物过程的准确建模.
结论:
- ML和DL为兽医科学提供了数据驱动的工具.
- 挑战包括数据限制和模型跨物种适用性.
- 未来的工作需要专门的算法和集成的智能系统,以适应性预防疾病.
- 结合强化和无监督学习,可以优化疫苗接种和检测新出现的病原体.
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