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Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworksdoi:10.1371/journal.pcbi.1007084 pmid:31295267 pmcid:PMC6622478 fatcat:othxoh2y6rfsbis7q5e2cacdrq