Glass Box UMAP: A Python package for interpretable UMAP via exact feature contributions

A benchmark for the next generation of genotype–phenotype mapping

Assembly and annotation artifacts can lead to problematic protein structural inferences

Biology needs to become prospective

Strategizing the search for bio-utility: A framework for evolution-integrated _in silico_ bioprospecting

MSA-based pLMs encode evolutionary distance but don’t reliably exploit it

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