M. D. Lehti-Shiu

Evolution of research topics and paradigms in plant sciences

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.

Genome-wide predictions of genetic redundancy in Arabidopsis thaliana

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.

Optimizing the use of gene expression data to predict plant metabolic pathway memberships

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.

Within and cross species predictions of plant specialized metabolism genes using transfer learning

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

Factors influencing gene family size variation among related species in a plant family

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