Equivalent linear mappings of deep networks are a promising path for biology

A quantitative-genetic decomposition of a neural network

From black box to glass box\: Making UMAP interpretable with exact feature contributions

Efficient GFP variant design with a simple neural network ensemble

Mapping the spectrum of archaeal protein sequence–structure relationships

G–P Atlas\: A neural network framework for mapping genotypes to many phenotypes

Do protein language models understand evolution? Mixed evidence from ancestral sequences and ESM2

Modeling human monogenic diseases using the choanoflagellate *Salpingoeca rosetta*

Modeling human monogenic diseases using the tunicate *Ciona intestinalis*

A framework for modeling human monogenic diseases by deploying organism selection

Graph neural networks\: A unifying predictive model architecture for evolutionary applications

Phylogenies and biological foundation models

Epistasis and deep learning in quantitative genetics

Cross-trait learning with a canonical transformer tops custom attention in genotype–phenotype mapping

Closing the divide between analysis and publication\: The notebook pub

Predicting antimicrobial resistance phenotypes across 7,000 *E. coli* genomes

Structure-based protein clustering sometimes, but not always, provides insight into protein function

Comparative phylogenomic analysis of chelicerates points to gene families associated with long-term suppression of host detection