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A mathematical model of erythropoiesis in mice and rats. Part 1: Structure of the model

, , , and . Cell and tissue kinetics, 22 (1): 13–30 (1989)

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A mathematical model of erythropoiesis in mice and rats. Part 2: Stimulated erythropoiesis, , , and . Cell and tissue kinetics, 22 (1): 31–49 (1989)Mathematische Modelle in der Hämatologie, , , , , and . Klinische Wochenschrift, 61 (19): 935–940 (1983)A mathematical model of erythropoiesis in mice and rats. Part 3: Suppressed erythropoiesis, , , and . Cell and tissue kinetics, 22 (1): 51–61 (1989)A mathematical model of erythropoiesis in mice and rats. Part 1: Structure of the model, , , and . Cell and tissue kinetics, 22 (1): 13–30 (1989)Optimized inhibitors of soluble epoxide hydrolase improve in vitro target residence time and in vivo efficacy, , , , , , , , , and 9 other author(s). Journal of Medicinal Chemistry, 57 (16): 7016-7030 (August 2014)Monte Carlo simulation of the Spearman-Kaerber TCID50., , and . J. Clin. Bioinform., (2012)Learning Cellular Automation Dynamics with Neural Networks., and . NIPS, page 631-638. Morgan Kaufmann, (1992)NLR-parser: rapid annotation of plant NLR complements., , , , and . Bioinform., 31 (10): 1665-1667 (2015)Genomic innovation for crop improvement., , , , , and . Nat., 543 (7645): 346-354 (2017)