Artificial Intelligence for Biology. (27th August 2021)

• Record Type: Journal Article
• Authors: Hassoun, Soha; Jefferson, Felicia; Shi, Xinghua; Stucky, Brian; Wang, Jin; Rosa, Epaminondas
• Is Part Of: Integrative and comparative biology. Volume 61:Number 6(2021)
• Page Start: 2267
• Page End: 2275
• Publication Date: 2021-08-27
• Subjects: Zoology -- Periodicals; Biology -- Periodicals; Physiology, Comparative -- Periodicals; 590.5
• DOI: 10.1093/icb/icab188
• Languages: English
• ISSNs: 1540-7063

Abstract

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.