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引用次数: 0
摘要
近年来,人工智能(AI)和机器学习(ML)的新方法(通常称为数据科学(DS))在数据驱动领域取得了许多进展,包括计算生物学、生物信息学、网络医学、精准医学和系统医学(He et al.,2019;Rajkomar等人,2019;Zou等人,2019)。鉴于技术创新的持续,将进一步导致在所有分子水平上进行新的高通量测量,可以预期,人工智能和ML在未来对医学和生物医学的重要性甚至会增加(Obermeyer和Emanuel,2016;EmmertStreib,2021)。因此,需要一个科学论坛来促进人工智能、ML和普通DS在分子医学中的方法发展和实际应用,使社区能够传播和讨论最近的结果。生物信息学和人工智能专业部分旨在提供这样一个论坛,发表关于分析所有类型的奥密克戎、临床和健康数据的文章,以增强我们对分子医学的理解。重点是应用或开发基于AI或ML方法的诊断、预后、预测或探索性研究的数据驱动方法。在图1中,我们概述了利用人工智能和机器学习来增强我们对分子医学知识的科学发现迭代过程。在下文中,我们讨论了其中几个主题,我们认为这些主题与分子医学中AI和ML的发展特别相关。