Aaron A. Best

ModelSEED v2 improves genome-scale metabolic model reconstruction with enhanced energy metabolism and better pathway curation, leading to more accurate and comprehensive models.

This study builds machine learning classifiers to predict variable gene presence in E. coli genomes, achieving high accuracy for most gene families, including poorly annotated and horizontally transferred genes.

The paper introduces the ModelSEED biochemistry database, which integrates metabolic annotations and supports the reconstruction and analysis of metabolic models for plants, fungi, and microbes.

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.

The paper improves a Bayesian method for gene activity state estimation by addressing limitations in handling outliers and modeling uncertainty in co-regulation networks.