Taylor H. Ferebee

Fishing for a reelGene: evaluating gene models with evolution and machine learning

reelGene evaluates gene models using machine learning and evolutionary data, finding 28% of maize gene models are incorrectly annotated or nonfunctional.

Extensive genome evolution distinguishes maize within a stable tribe of grasses

The paper examines genome evolution in maize and related grasses, revealing variable outcomes of polyploidy across species.

Current genomic deep learning architectures generalize across grass species but not alleles

The paper benchmarks deep learning models on genomic data across 18 grass species, finding they generalize across species but not across alleles within species.

Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize

This study evaluates regulatory network-based machine learning for gene expression prediction in maize using Arabidopsis thaliana networks, finding that simple models outperform complex ones within species but complex models are better for cross-species predictions.