Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning

Abstract GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling m...

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Bibliographic Details
Main Authors: Can Chen, Chen Liao, Yang-Yu Liu
Format: Article
Language:English
Published: Nature Portfolio 2023-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-023-38110-7