What is the current state of research on using graph neural networks to reconstruct genome-scale metabolic networks?
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Graph neural networks (GNNs) have been explored as a tool for reconstructing genome-scale metabolic networks. GNN-based layout phases have shown the ability to reconstruct longer sequences compared to naive search algorithms or heuristics used in de novo genome assemblers . The use of GNNs in this task has demonstrated no significant difference in compute time on sequences up to 10 Mbp in length . This approach has the potential to improve the accuracy of reconstruction and provide additional insights into the contribution of each constructed metabolic feature . However, it is important to note that the current state of research on using GNNs for reconstructing genome-scale metabolic networks is limited to a few studies, and further investigation is needed to fully understand their potential and limitations in this context.
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19 Citations | The provided paper does not mention the use of graph neural networks for reconstructing genome-scale metabolic networks. |
| The provided paper does not mention the use of graph neural networks to reconstruct genome-scale metabolic networks. | |
22 May 2023 | The provided paper does not discuss the use of graph neural networks to reconstruct genome-scale metabolic networks. |
22 May 2023 | The provided paper does not discuss the use of graph neural networks to reconstruct genome-scale metabolic networks. |
1 Citations | The provided paper does not mention the use of graph neural networks for reconstructing genome-scale metabolic networks. |
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