Machine learning for evolutionary genetics and molecular evolution
Abstract
Over the past decade, the rapid expansion of large-scale data and advances in computational power have allowed machine learning (ML), especially deep learning, to reshape many areas of biological research. Evolutionary genetics and molecular evolution are also poised for a similar transformation. In this review, we discuss key advances and ongoing challenges in applying ML to the study of genetics and evolution, and we highlight the potential of artificial intelligence to connect genotype, phenotype, and evolutionary history.
Fields of Study & Subjects
Evolution and Genetic Dynamics
Machine Learning in Bioinformatics
Evolutionary Algorithms and Applications
Publication details
- Journal / source
- Trends in Genetics
- Publisher
- Elsevier BV
- Volume
- 42
- Issue
- 5
- Pages
- 423–433
- Publication date
- 24 March 2026
- Language
- EN