Caroline M. Holmes

This paper introduces two methods, SLED and GTD, to infer modular structure in genotype-phenotype maps by combining epistasis and pleiotropy, showing they yield similar modular architectures in yeast data.

The paper uses a broad range of yeast genotypes to assess epistasis in complex traits, finding limited contributions of epistasis to phenotypic variance.

This paper applies attention-based models to predict phenotype from genotype, showing improved performance in epistatic regimes and enabling transfer learning across environments.

The paper shows that heavy-tailed neuronal connectivity and clustering emerge from a minimal model of Hebbian self-organization, confirmed in animal connectomes.

The paper analyzes retinal population codes in natural scenes to show that stimulus-invariant population structure contributes to scene identity encoding.

The paper improves a mutual information estimator by expanding its applicability and adding methods for bias verification, variance estimation, and parameter selection, tested on synthetic and biological data.