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生物信息学中的人工智能:一项调查

Jiyue Jiang1,2, Yunke Li1,3, Shiwei Cao1,4

  • 1Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, 510005 Guangzhou Province, China.

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概括

人工智能 (AI) 正在彻底改变生物信息学,因为它能够分析大量的生物数据集,用于诸如蛋白质结构预测和个性化医学等应用. 这篇评论详细介绍了人工智能.

关键词:
人工智能的人工智能是人工智能.生物信息学是一种生物信息学.调查调查调查调查调查调查调查调查

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 生物数据的指数增长来自高通量测序和多组学.
  • 管理,分析和解释复杂的大规模生物数据集的挑战.
  • 人工智能的进步,特别是深度学习和强化学习,为生物信息学提供了新的分析工具.

研究的目的:

  • 系统地审查人工智能在生物信息学中的最新进展和应用.
  • 讨论各种AI方法 (传统的ML,深度学习,强化学习) 的合适场景和优势.
  • 突出人工智能对生物研究和精准医学的变革性影响.

主要方法:

  • 审查最近的研究进展和人工智能技术在生物信息学中的代表性应用.
  • 讨论传统的机器学习,深度学习和强化学习方法.
  • 分析AI在特定任务中的进步,例如生物医学文本挖掘和多式联通电话集成.

主要成果:

  • 人工智能取得了具有里程碑意义的结果:精确的蛋白质结构预测 (0.96 Å),强大的单细胞建模 (AvgBIO ≈ 0.82),高蛋白质设计成功率 (高达 92%) 和敏感的癌症检测 (AUC ≈ 0.93).
  • 人工智能应用于DNA/RNA/蛋白质序列分析,3D结构阐明,功能注释,多omics集成和个性化药物设计.
  • 主要挑战包括数据噪声,稀疏性,长序列建模,多式联络整合,可解释性和道德问题.

结论:

  • 人工智能通过复杂的数据分析显著推进了生物研究和精准医学.
  • 未来的方向包括大规模数据挖掘,跨领域概括以及药物设计方面的创新.
  • 倡导开放和协作型的研究生态系统,以进一步发展生物信息学中的人工智能.