Motivation: Genomic prediction is now an essential technique in breeding and medicine, and it is interesting to see how omics data can be used to improve prediction accuracy. Precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis; however, the method consists of multiple steps that possibly degrade prediction accuracy.
Results: We proposed a Bayesian model that integrates all steps and jointly infers all fluxes of reactions related to biomass production. The proposed model showed higher accuracies than methods compared both in simulated and real data. The findings support the previous excellent idea that metabolic network information can be used for prediction.
Availability And Implementation: All R and stan scripts to reproduce the results of this study are available at https://github.com/Onogi/MetabolicModeling.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11312854 | PMC |
| http://dx.doi.org/10.1093/bioadv/vbad106 | DOI Listing |
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