We also review variant effect prediction evaluations that have been performed to date on genomic deep learning models, highlighting strengths and limitations of current models and the need for more comprehensive evaluation. 3/4

We cover two popular genomic deep learning modeling paradigms — supervised sequence-to-activity models and self-supervised genomic language models — and describe practical considerations for using both types of models to make variant effect predictions. 2/4