Aditi T. Merchant

This paper demonstrates that in-context learning in the genome language model Evo 2 enables efficient biological sequence design without additional training.

This paper introduces Proto, a high-level programming language for generative biology, and demonstrates its use in designing and validating biological systems.

This paper introduces a genomic language model, Evo, capable of generating functional de novo genes through semantic mining, validated by experimental testing of anti-CRISPR proteins and toxin-antitoxin systems.

Fine-tuned Evo 1 and Evo 2 genome language models designed complete ΦX174-like phage genomes; 16 of 285 assembled designs were viable, and some outperformed ΦX174 in competition.

A 40B-parameter DNA model trained on 9.3 trillion tokens scores coding and noncoding variants zero-shot, exposes interpretable sequence features, and writes chromosome-scale sequences.