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Autor:
Santaolalla, Aida, Garmo, Hans, Grigoriadis, Anita, Ghuman, Sundeep, Hammar, Niklas, Jungner, Ingmar, Walldius, Göran, Lambe, Mats, Holmberg, Lars, Van Hemelrijck, Mieke
Publikováno v:
BMC Molecular and Cell Biology
Santa Olalla, A, Van Hemelrijck, M, Holmberg, L H, Grigoriadis, A, Ghuman, S, Hammar, N, Lambe, M, Garmo, H G, Walldius, G & Jungner, I 2019, ' Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis ', BMC Molecular Biology, vol. 20, no. 1, 28 . https://doi.org/10.1186/s12860-019-0210-7
BMC Molecular and Cell Biology, Vol 20, Iss 1, Pp 1-15 (2019)
Santa Olalla, A, Van Hemelrijck, M, Holmberg, L H, Grigoriadis, A, Ghuman, S, Hammar, N, Lambe, M, Garmo, H G, Walldius, G & Jungner, I 2019, ' Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis ', BMC Molecular Biology, vol. 20, no. 1, 28 . https://doi.org/10.1186/s12860-019-0210-7
BMC Molecular and Cell Biology, Vol 20, Iss 1, Pp 1-15 (2019)
Background Metabolites are genetically and environmentally determined. Consequently, they can be used to characterize environmental exposures and reveal biochemical mechanisms that link exposure to disease. To explore disease susceptibility and impro