• Corpus ID: 10852805

Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling

@article{Chien2017PredictingTP,
  title={Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling},
  author={Jennifer Chien and Peter E. Larsen},
  journal={arXiv: Genomics},
  year={2017}
}

• Jennifer Chien, Peter E. Larsen
• Published 12 January 2017
• Biology
• arXiv: Genomics

Plants rarely occur in isolated systems. Bacteria can inhabit either the endosphere, the region inside the plant root, or the rhizosphere, the soil region just outside the plant root. Our goal is to understand if using genomic data and media dependent metabolic model information is better for training machine learning of predicting bacterial ecological niche than media independent models or pure genome based species trees. We considered three machine learning techniques: support vector machine, non-negative matrix factorization, and artificial neural networks. In all three machine-learning approaches, the media-based metabolic models and flux balance analyses were more effective at predicting bacterial niche than the genome or PRMT models. Support Vector Machine trained on a minimal media base with Mannose, Proline and Valine was most predictive of all models and media types with an f-score of 0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that media-based metabolic modeling provides a holistic view of the metabolome, allowing machine learning algorithms to highlight the differences between and categorize endosphere and rhizosphere bacteria. There was no single media type that best highlighted differences between endosphere and rhizosphere bacteria metabolism and therefore no single enzyme, reaction, or compound that defined whether a bacteria's origin was of the endosphere or rhizosphere.