Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2016
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/99ed3ae0d5e148ff8d397f4572e0ceba |
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| Sumario: | Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations. |
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