Deciphering "the language of nature": A transformer-based language model for deleterious mutations in proteins
Graphical abstract
Various machine-learning models, including deep neural network models, have already been developed to predict deleteriousness of missense (non-synonymous) mutations. Potential improvements to the current state of the art, however, may still benefit from a fresh look at the biological problem using more sophisticated self-adaptive machine-learning approaches. Recent advances in the field of natural language processing show that transformer models—a type of deep neural network—to be particularly powerful at modeling sequence information with context dependence.
