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Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning
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 methods ty...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130184/ https://www.ncbi.nlm.nih.gov/pubmed/37185345 http://dx.doi.org/10.1038/s41467-023-38110-7 |