DOI: 10.1101/460071Nov 7, 2018Paper

Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning

BioRxiv : the Preprint Server for Biology
Gregory Leonard Medlock, Jason A Papin

Abstract

Mechanistic models are becoming common in biology and medicine. These models are often more generalizable than data-driven models because they explicitly represent biological knowledge, enabling simulation of scenarios that were not used to construct the model. While this generalizability has advantages, it also creates a dilemma: how should model curation efforts be focused to improve model performance? Here, we develop a machine learning-guided solution to this problem for genome-scale metabolic models. We generate an ensemble of candidate models consistent with experimental data, then perform in silico ensemble simulations for which improved predictiveness is desired. We apply unsupervised and supervised learning to the simulation output to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. The resulting structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach, called Automated Metabolic Model Ensemble-Driven Elimination of Uncertainty with Statistical learning (AMMEDEUS), by applying it to 29 bacterial species to identify curation targets that improve gene essentiality predictions...Continue Reading

Citations

Aug 9, 2019·Genome Biology·Sebastián N MendozaBas Teusink

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Methods Mentioned

BETA
PCA
gene knockouts

Software Mentioned

optlang
Biolog
posthocs
learn
GENRE
RandomForestClassifier
GNU
SciPy
AMMEDEUS
cobrapy

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