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.
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2016-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/ncomms13090 |
| Summary: | 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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| ISSN: | 2041-1723 |