Keeping generative artificial intelligence reliable in omics biology
Abstract
Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data collection is hampered by considerable experimental constraints. However, it can also "hallucinate"-i.e., create data that are too original to be realistic-which is a critical issue in molecular biology, as hallucinated inferences could have devastating consequences. The author thus explores various use cases to mitigate hallucination-induced risks and to safely unleash the full potential of generative methods.
Fields of Study & Subjects
Bioinformatics and Genomic Networks
Genetics
Bioinformatics
and Biomedical Research
Computational Drug Discovery Methods
Publication details
- Journal / source
- Patterns
- Publisher
- Elsevier BV
- Volume
- 7
- Issue
- 1
- Pages
- 101417–101417
- Publication date
- 1 January 2026
- Language
- EN