Department of Computer Science, Tufts University, Medford, MA 02155, USA.
Biology Academic Department, Fort Valley State University, Fort Valley, GA 31030, USA.
Integr Comp Biol. 2022 Feb 5;61(6):2267-2275. doi: 10.1093/icb/icab188.
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.
尽管人们努力将不同生物学子学科的研究整合在一起，但整合的规模仍然有限。我们假设，未来专门为生物科学设计的人工智能 (AI) 技术将有助于实现生物学的再整合。AI 技术不仅将使我们能够以前所未有的规模收集、连接和分析数据，还将构建涵盖各个子学科的综合预测模型。它们将使有针对性（测试特定假设）和无针对性的发现成为可能。人工智能将成为生物学的交叉技术，增强我们在各个尺度上进行生物学研究的能力。我们预计，人工智能将像 20 世纪统计学改变生物学一样，在 21 世纪彻底改变生物学。然而，困难重重，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，以及更适合生物学的新的预测和可解释的人工智能模型，而不是现有的机器学习和人工智能技术。开发工作将需要生物学和计算科学家之间的紧密合作。本白皮书为生物学人工智能提供了一个愿景，并强调了一些挑战。
