S. B. King

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

This paper aligns a protein generative model with experimental fitness using Direct Preference Optimization, improving stability prediction and enabling new applications.

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.

7B-parameter convolution-attention DNA model with 131 kb context, trained on prokaryotic genomes, performs zero-shot fitness and essentiality prediction and generates Cas loci, transposons, and 650 kb sequences.

This paper introduces CloneSelect, a multi-kingdom genetic barcoding system that enables precise isolation of target clones using CRISPR base editing to trigger reporter gene expression.

The paper introduces GPS-seq, a theoretical framework for spatial transcriptomics that uses DNA barcodes to enable large-scale, optics-free imaging of tissue sections at single-cell resolution.

This paper models SARS-CoV-2 spike protein evolution using a neural network and Gaussian process to predict future variants up to five months in advance.