M. D. Lehti-Shiu

The paper uses machine learning and language models to analyze plant science citations and track the evolution of research topics and paradigms over 50 years.

This study develops a machine learning model to predict genetic redundancy in Arabidopsis thaliana using diverse features, identifying key predictors like recent duplication and stress-related expression.

This study predicts plant metabolic pathway memberships using gene expression data and machine learning, finding that unsupervised learning and expression similarity outperform naive methods in some cases.

The paper uses transfer learning to predict specialized metabolism genes in tomato by leveraging annotations from Arabidopsis thaliana, improving prediction accuracy.

The paper investigates gene family size variation in Solanaceae species, finding relationships between gene function, duplication mechanisms, and evolutionary rates.