By incorporating sequence homology and context associations, global probabilistic approaches to annotate genome-scale metabolic networks can substantially improve the accuracy of biochemical predictions, revealing potential functionality and directing experimental validation.
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References
Plata, G. et al. Nat. Chem. Biol. 8, 848–854 (2012).
Lee, D., Redfern, O. & Orengo, C. Nat. Rev. Mol. Cell Biol. 8, 995–1005 (2007).
Chang, A., Scheer, M., Grote, A., Schomburg, I. & Schomburg, D. Nucleic Acids Res. 37, D588–D592 (2009).
Kanehisa, M. et al. Nucleic Acids Res. 36, D480–D484 (2008).
Caspi, R. et al. Nucleic Acids Res. 36, D623–D631 (2008).
Karp, P.D. et al. Brief. Bioinform. 11, 40–79 (2010).
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Suthers, P., Maranas, C. Orchestrating hi-fi annotations. Nat Chem Biol 8, 810–811 (2012). https://doi.org/10.1038/nchembio.1067
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DOI: https://doi.org/10.1038/nchembio.1067
