Member since Dec 2025
Interpretable neural networks reveal global epistasis in yeast
A benchmark for the next generation of genotype–phenotype mapping
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
Epistasis and deep learning in quantitative genetics
Cross-trait learning with a canonical transformer tops custom attention in genotype–phenotype mapping
Predicting antimicrobial resistance phenotypes across 7,000 E. coli genomes