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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-04-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-023-38110-7 |