Harrington, Y.A.; Fortaner-Uyà , L.; Paolini, M.; Poletti, S.; Lorenzi, C.; Spadini, S.; Melloni, E.M.T.; Agnoletto, E.; Zanardi, R.; Colombo, C.;
et al. Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample. Genes 2024, 15, 1517.
https://doi.org/10.3390/genes15121517
AMA Style
Harrington YA, Fortaner-Uyà L, Paolini M, Poletti S, Lorenzi C, Spadini S, Melloni EMT, Agnoletto E, Zanardi R, Colombo C,
et al. Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample. Genes. 2024; 15(12):1517.
https://doi.org/10.3390/genes15121517
Chicago/Turabian Style
Harrington, Yasmin A., Lidia Fortaner-Uyà , Marco Paolini, Sara Poletti, Cristina Lorenzi, Sara Spadini, Elisa M. T. Melloni, Elena Agnoletto, Raffaella Zanardi, Cristina Colombo,
and et al. 2024. "Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample" Genes 15, no. 12: 1517.
https://doi.org/10.3390/genes15121517
APA Style
Harrington, Y. A., Fortaner-Uyà , L., Paolini, M., Poletti, S., Lorenzi, C., Spadini, S., Melloni, E. M. T., Agnoletto, E., Zanardi, R., Colombo, C., & Benedetti, F.
(2024). Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample. Genes, 15(12), 1517.
https://doi.org/10.3390/genes15121517
