SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions
Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04281-7 |