AI in genetics

Recent advances in machine learning (ML) especially deep learning have enabled breakthroughs in biology and are poised to play an important role in evolutionary biology. 

ML models are increasingly capable of connecting genetic variation to molecular function, integrating structural, regulatory, and phenotypic data at multiple scales.

ML models can now be used directly on raw SNPs, haplotypes, or allele frequency spectra, reducing reliance on summary statistics and enabling the discovery of previously unrecognized evolutionary patterns.

Emerging approaches move beyond correlation, using causal inference to generate mechanistic insights into evolutionary processes.

Integrating multiomics data through ML is revealing hidden layers of complexity, such as epistasis, regulatory evolution, and phenotypic plasticity, that shape adaptation and diversification.

https://www.cell.com/trends/genetics/fulltext/S0168-9525(26)00032-6
https://sciencemission.com/Machine-learning-for-evolutionary-genetics