Biological databases in the age of generative artificial intelligence

doi:10.1093/bioadv/vbaf044

Editorial

. 2025 Mar 20;5(1):vbaf044.

doi: 10.1093/bioadv/vbaf044. eCollection 2025.

Biological databases in the age of generative artificial intelligence

Mihai Pop¹, Teresa K Attwood², Judith A Blake³, Philip E Bourne⁴, Ana Conesa⁵, Terry Gaasterland⁶, Lawrence Hunter⁷, Carl Kingsford⁸, Oliver Kohlbacher⁹, Thomas Lengauer¹⁰, Scott Markel¹¹, Yves Moreau¹², William S Noble¹³, Christine Orengo¹⁴, B F Francis Ouellette¹⁵, Laxmi Parida¹⁶, Natasa Przulj^{17

18

19

20}, Teresa M Przytycka²¹, Shoba Ranganathan²², Russell Schwartz^{8

23}, Alfonso Valencia^{18

19}, Tandy Warnow²⁴

Affiliations

¹ Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States.
² Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom.
³ The Jackson Laboratory, Bar Harbor, ME 04609, United States.
⁴ School of Data Science, The University of Virginia, Charlotesville, VA 22904, United States.
⁵ Institute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain.
⁶ Bioinformatics & Systems Biology Graduate Program, La Jolla, CA 92093, United States.
⁷ Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States.
⁸ Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States.
⁹ Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72076, Germany.
¹⁰ Max Planck Institute for Informatics and Saarland Informatics Campus, Saarbrücken 66123, Germany.
¹¹ Dassault Systèmes BIOVIA, San Diego, CA 92121, United States.
¹² Elektrotechniek ESAT-STADIUS, University of Leuven, Leuven 3000, Belgium.
¹³ Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States.
¹⁴ Department of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom.
¹⁵ Montreal, QC H2T 2W5, Canada.
¹⁶ IBM T J Watson Research, Yorktown Heights, NY 10598, United States.
¹⁷ Computational Biology Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi SE45 05, United Arab Emirates.
¹⁸ Barcelona Supercomputing Center, Barcelona 08034, Spain.
¹⁹ Institución Catalana de Investigación y Estudios Avanzados (ICREA), Barcelona 08010, Spain.
²⁰ Department of Computer Science, University College London, London WC1E 6EA, United Kingdom.
²¹ Computational Biology Branch, Division of Intramural Research, National Library of Medicine, Bethesda, MD 20894, United States.
²² Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
²³ Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, United States.
²⁴ School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.

PMID: 40177265
PMCID: PMC11964588
DOI: 10.1093/bioadv/vbaf044

Editorial

Biological databases in the age of generative artificial intelligence

Mihai Pop et al. Bioinform Adv. 2025.

. 2025 Mar 20;5(1):vbaf044.

doi: 10.1093/bioadv/vbaf044. eCollection 2025.

Authors

Affiliations

¹ Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States.
² Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom.
³ The Jackson Laboratory, Bar Harbor, ME 04609, United States.
⁴ School of Data Science, The University of Virginia, Charlotesville, VA 22904, United States.
⁵ Institute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain.
⁶ Bioinformatics & Systems Biology Graduate Program, La Jolla, CA 92093, United States.
⁷ Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States.
⁸ Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States.
⁹ Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72076, Germany.
¹⁰ Max Planck Institute for Informatics and Saarland Informatics Campus, Saarbrücken 66123, Germany.
¹¹ Dassault Systèmes BIOVIA, San Diego, CA 92121, United States.
¹² Elektrotechniek ESAT-STADIUS, University of Leuven, Leuven 3000, Belgium.
¹³ Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States.
¹⁴ Department of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom.
¹⁵ Montreal, QC H2T 2W5, Canada.
¹⁶ IBM T J Watson Research, Yorktown Heights, NY 10598, United States.
¹⁷ Computational Biology Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi SE45 05, United Arab Emirates.
¹⁸ Barcelona Supercomputing Center, Barcelona 08034, Spain.
¹⁹ Institución Catalana de Investigación y Estudios Avanzados (ICREA), Barcelona 08010, Spain.
²⁰ Department of Computer Science, University College London, London WC1E 6EA, United Kingdom.
²¹ Computational Biology Branch, Division of Intramural Research, National Library of Medicine, Bethesda, MD 20894, United States.
²² Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
²³ Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, United States.
²⁴ School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.

PMID: 40177265
PMCID: PMC11964588
DOI: 10.1093/bioadv/vbaf044

Abstract

Summary: Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.

Availability and implementation: Not applicable.

PubMed Disclaimer

Conflict of interest statement

None declared.

Cited by

Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.
Athanasopoulou K, Michalopoulou VI, Scorilas A, Adamopoulos PG. Athanasopoulou K, et al. Curr Issues Mol Biol. 2025 Jun 19;47(6):470. doi: 10.3390/cimb47060470. Curr Issues Mol Biol. 2025. PMID: 40699869 Free PMC article. Review.

References

1. Alemohammad S, Casco-Rodriguez J, Luzi L et al. Self-consuming generative models go MAD. In: Proceedings of The Twelfth International Conference on Learning Representation, ICLR 2024, Vienna, Austria, 2024.
1. Arkin AP, Cottingham RW, Henry CS et al. KBase: the United States department of energy systems biology knowledgebase. Nat Biotechnol 2018;36:566–9. - PMC - PubMed
1. Attwood TK, Blackford S, Brazas MD et al. A global perspective on evolving bioinformatics and data science training needs. Brief Bioinform 2019;20:398–404. - PMC - PubMed
1. Attwood TK, Bongcam-Rudloff E, Brazas ME et al. ; GOBLET Consortium. GOBLET: the global organisation for bioinformatics learning, education and training. PLoS Comput Biol 2015;11:e1004143. - PMC - PubMed
1. Attwood TK, Kell DB, McDermott P et al. Calling international rescue: knowledge lost in literature and data landslide! Biochem J 2009;424:317–33. - PMC - PubMed

Publication types

Actions

LinkOut - more resources

Full Text Sources
- PubMed Central
- Silverchair Information Systems
Research Materials
- NCI CPTC Antibody Characterization Program

[1] Alemohammad S, Casco-Rodriguez J, Luzi L et al. Self-consuming generative models go MAD. In: Proceedings of The Twelfth International Conference on Learning Representation, ICLR 2024, Vienna, Austria, 2024.

[2] Alemohammad S, Casco-Rodriguez J, Luzi L et al. Self-consuming generative models go MAD. In: Proceedings of The Twelfth International Conference on Learning Representation, ICLR 2024, Vienna, Austria, 2024.

[3] Arkin AP, Cottingham RW, Henry CS et al. KBase: the United States department of energy systems biology knowledgebase. Nat Biotechnol 2018;36:566–9. - PMC - PubMed

[4] Arkin AP, Cottingham RW, Henry CS et al. KBase: the United States department of energy systems biology knowledgebase. Nat Biotechnol 2018;36:566–9. - PMC - PubMed

[5] Attwood TK, Blackford S, Brazas MD et al. A global perspective on evolving bioinformatics and data science training needs. Brief Bioinform 2019;20:398–404. - PMC - PubMed

[6] Attwood TK, Blackford S, Brazas MD et al. A global perspective on evolving bioinformatics and data science training needs. Brief Bioinform 2019;20:398–404. - PMC - PubMed

[7] Attwood TK, Bongcam-Rudloff E, Brazas ME et al. ; GOBLET Consortium. GOBLET: the global organisation for bioinformatics learning, education and training. PLoS Comput Biol 2015;11:e1004143. - PMC - PubMed

[8] Attwood TK, Bongcam-Rudloff E, Brazas ME et al. ; GOBLET Consortium. GOBLET: the global organisation for bioinformatics learning, education and training. PLoS Comput Biol 2015;11:e1004143. - PMC - PubMed

[9] Attwood TK, Kell DB, McDermott P et al. Calling international rescue: knowledge lost in literature and data landslide! Biochem J 2009;424:317–33. - PMC - PubMed

[10] Attwood TK, Kell DB, McDermott P et al. Calling international rescue: knowledge lost in literature and data landslide! Biochem J 2009;424:317–33. - PMC - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Biological databases in the age of generative artificial intelligence

Affiliations

Biological databases in the age of generative artificial intelligence

Authors

Affiliations

Abstract

Conflict of interest statement

Similar articles

Cited by

References

Publication types

LinkOut - more resources

Full Text Sources

Research Materials