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AbstractDiverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms rely on preprocessing - identifying a list of high confidence (core) reactions from transcriptomics. Studies have shown parameters related to preprocessing, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrarydoi:10.1101/594861 fatcat:524ooqmte5euhd7g7engcf7lhq