Artificial Intelligence for Biology
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.
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AlphaFold combines deep learning innovations to predict three-dimensional protein structures at or near experimental scale resolution.
“AlphaFold has combined numerous deep learning innovations to predict the three-dimensional (3D) structures of proteins at or near experimental scale resolution”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [1]
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AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
- Authors
- Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa, E.
- Affiliations
- Tufts University (US)
- Fort Valley State University (US)
- Temple University (US)
- Florida Museum of Natural History (US)
- University of Tennessee at Chattanooga (US)
- Illinois State University (US)
- Corresponding author
- Rosa, Epaminondas
- Venue
- Integrative and Comparative Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-08-27
- Volume
- 61
- Issue
- 6
- Pages
- 2267-2275
- ISSN
- 1540-7063 (ISSN-L)
- 1557-7023
- OpenAlex
- W3194290548
- Cited by
- 64
- References
- 67
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 20 · 2023: 12 · 2022: 6 · 2021: 1
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Integrative and Comparative Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NSF (CCF-1909536, 1939739, 1900572, 1750632, 1951345)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- prediction model, data curation, data assembly, hypothesis testing, computational biology, biological theory, machine learning in biology
- MeSH terms
- Machine Learning, Animals, Artificial Intelligence, Biology, Technology
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