Artificial Intelligence for Biology
Artificial Intelligence for Biology
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DOI：
10.1093/icb/icab188
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发表时间：
2022-02-05
影响因子：
2.6
通讯作者：
Rosa, Epaminondas Jr Jr
中科院分区：
文献类型：
--
作者：
Hassoun, Soha;Jefferson, Felicia;Rosa, Epaminondas Jr Jr
概要 尽管努力整合生物学不同分支的研究，但整合的规模仍然有限。我们假设专门适用于生物科学的未来几代人工智能（AI）技术将有助于实现生物学的重新整合。人工智能技术不仅使我们能够以前所未有的规模收集、连接和分析数据，而且还能够构建跨越各个子学科的全面预测模型。它们将使有针对性的（测试特定假设）和无针对性的发现成为可能。生物学领域的人工智能将成为一种跨领域技术，将增强我们进行各个规模的生物学研究的能力。我们预计人工智能将在 21 世纪彻底改变生物学，就像统计学在 20 世纪改变生物学一样。然而，困难有很多，包括数据管理和组装、以连接子学科的理论形式发展新科学，以及比现有机器学习和人工智能技术更适合生物学的新预测和可解释人工智能模型。开发工作需要生物学和计算科学家之间的密切合作。本白皮书提供了生物学人工智能的愿景，并强调了一些挑战。
Synopsis Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect, and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines. They will make possible both targeted (testing specific hypotheses) and untargeted discoveries. AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century. The difficulties, however, are many, including data curation and assembly, development of new science in the form of theories that connect the subdisciplines, and new predictive and interpretable AI models that are more suited to biology than existing machine learning and AI techniques. Development efforts will require strong collaborations between biological and computational scientists. This white paper provides a vision for AI for Biology and highlights some challenges.