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人工智能方法在感染生物学研究中的研究方法

Jacob Marcel Anter1,2, Artur Yakimovich3,4,5

  • 1Center for Advanced Systems Understanding (CASUS), Görlitz, Germany.

Methods in molecular biology (Clifton, N.J.)
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概括

感染生物学中的人工智能 (AI) 从简单的自动化演变为多功能"瑞士军刀". 这种由COVID-19大流行加速的转变,使得使用各种数据类型对宿主-病原体相互作用进行了复杂的分析.

关键词:
人工智能的人工智能是人工智能.计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.主体病原体相互作用感染生物学 感染生物学机器学习 机器学习自然语言处理自然语言处理.病原体是一种病原体.蛋白质蛋白质相互作用病毒学 病毒学

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科学领域:

  • 感染生物学 感染生物学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 感染生物学中的AI应用仅限于任务自动化 (
  • 机器分类机分类机分类机分类机
  • 由于对量化方法的抵制性而导致的) 范式).
  • 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 流行病催化了该领域的快速AI进步.

研究的目的:

  • 概述AI在感染生物学中的演变,从基本的自动化到一个多功能工具.
  • 瑞士军用刀 瑞士军用刀
  • 一个范式).
  • 通过图像,分子和语言数据来说明AI应用程序,以了解宿主-病原体相互作用.
  • 通过解释术语和提供实用指南,为感染生物学家揭开AI的神秘面.

主要方法:

  • 在感染生物学中对人工智能应用进行审查和综合.
  • 在不同数据模式 (图像,分子数据,语言数据) 中展示AI使用案例.
  • 解释基本的人工智能术语和子领域关系.
  • 包括人工智能实施的实用指南 (软件安装,数据准备,模型利用).

主要成果:

  • 感染生物学中的AI已经成熟,超出了简单的自动化,以应对复杂的挑战.
  • 成功的AI应用程序被证明用于分析各种数据类型,有助于主机-病原体相互作用研究.
  • 过渡到一个多功能的人工智能工具包使感染生物学家具有先进的分析能力.

结论:

  • 人工智能已成为感染生物学中不可或缺的多功能工具,从基本的自动化转向复杂的解决问题.
  • COVID-19大流行加速了人工智能的采用,并证明了它在应对关键传染病挑战方面的潜力.
  • 这项工作为感染生物学家提供了基础的理解和实际指导,以便将AI整合到他们的研究中.