BROMBACHER, Eva, HACKENBERG, Maren, KREUTZ, Clemens, BINDER, Harald und TREPPNER, Martin, 2022. The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Freiburg: Universität.
Elsevier - Harvard (with titles)Brombacher, E., Hackenberg, M., Kreutz, C., Binder, H., Treppner, M., 2022. The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Universität, Freiburg. https://doi.org/10.3389/fmolb.2022.962644
American Psychological Association 7th editionBrombacher, E., Hackenberg, M., Kreutz, C., Binder, H., & Treppner, M. (ca. 2022). The performance of deep generative models for learning joint embeddings of single-cell multi-omics data [Cd]. Universität. https://doi.org/10.3389/fmolb.2022.962644
Springer - Basic (author-date)Brombacher E, Hackenberg M, Kreutz C, Binder H, Treppner M (2022) The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Universität, Freiburg
Juristische Zitierweise (Stüber) (Deutsch)Brombacher, Eva/ Hackenberg, Maren/ Kreutz, Clemens/ Binder, Harald/ Treppner, Martin, The performance of deep generative models for learning joint embeddings of single-cell multi-omics data, Freiburg 2022.