Machine-learning methods applied to integrated transcriptomic data from bovine blastocysts and elongating conceptuses to identify genes predictive of embryonic competence.

MLA

Rabaglino, Maria Belen, et al. “Machine-Learning Methods Applied to Integrated Transcriptomic Data from Bovine Blastocysts and Elongating Conceptuses to Identify Genes Predictive of Embryonic Competence.” FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology, vol. 37, no. 3, Mar. 2023, p. e22809. EBSCOhost, https://doi.org/10.1096/fj.202201977R.

APA

Rabaglino, M. B., Salilew-Wondim, D., Zolini, A., Tesfaye, D., Hoelker, M., Lonergan, P., & Hansen, P. J. (2023). Machine-learning methods applied to integrated transcriptomic data from bovine blastocysts and elongating conceptuses to identify genes predictive of embryonic competence. FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology, 37(3), e22809. https://doi.org/10.1096/fj.202201977R

Chicago

Rabaglino, Maria Belen, Dessie Salilew-Wondim, Adriana Zolini, Dawit Tesfaye, Michael Hoelker, Pat Lonergan, and Peter J Hansen. 2023. “Machine-Learning Methods Applied to Integrated Transcriptomic Data from Bovine Blastocysts and Elongating Conceptuses to Identify Genes Predictive of Embryonic Competence.” FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology 37 (3): e22809. doi:10.1096/fj.202201977R.