Cite Them Right 11th edition - Harvard

Settelmeier, J., Goetze, S., Boshart, J., Fu, J., Khoo, A., Steiner, S.N., Gesell, M., Hammer, J., Schüffler, P.J., Salimova, D., Pedrioli, P.G.A. und Wollscheid, B. (2025) Multi Omics Agent : : guided extreme gradient-boosted decision trees-based approaches for biomarker-Candidate discovery in multiomics data [cd]. Freiburg: Universität. doi:10.1021/acs.jproteome.4 c01066.

Chicago Manual of Style 17th edition (full note)

Settelmeier, Jens, Sandra Goetze, Julia Boshart, Jianbo Fu, Amanda Khoo, Sebastian N. Steiner, Martin Gesell, Jacqueline Hammer, Peter J. Schüffler, Diyora Salimova, Patrick G. A. Pedrioli, und Bernd Wollscheid. Multi Omics Agent : : guided extreme gradient-boosted decision trees-based approaches for biomarker-Candidate discovery in multiomics data. Cd. Freiburg: Universität, [2025?], Freiburg: Universität, [2025?]. https://doi.org/10.1021/acs.jproteome.4 c01066.

American Psychological Association 7th edition

Settelmeier, J., Goetze, S., Boshart, J., Fu, J., Khoo, A., Steiner, S. N., Gesell, M., Hammer, J., Schüffler, P. J., Salimova, D., Pedrioli, P. G. A., & Wollscheid, B. (ca. 2025). Multi Omics Agent : : guided extreme gradient-boosted decision trees-based approaches for biomarker-Candidate discovery in multiomics data [Cd]. Universität. https://doi.org/10.1021/acs.jproteome.4 c01066

Modern Language Association 9th edition

Settelmeier, J., S. Goetze, J. Boshart, J. Fu, A. Khoo, S. N. Steiner, M. Gesell, J. Hammer, P. J. Schüffler, D. Salimova, P. G. A. Pedrioli, und B. Wollscheid. Multi Omics Agent : : guided extreme gradient-boosted decision trees-based approaches for biomarker-Candidate discovery in multiomics data. cd, Universität, 2025, https://doi.org/10.1021/acs.jproteome.4 c01066.

ISO-690 (author-date, Deutsch)

SETTELMEIER, Jens, Sandra GOETZE, Julia BOSHART, Jianbo FU, Amanda KHOO, Sebastian N. STEINER, Martin GESELL, Jacqueline HAMMER, Peter J. SCHÜFFLER, Diyora SALIMOVA, Patrick G. A. PEDRIOLI und Bernd WOLLSCHEID, 2025. Multi Omics Agent : : guided extreme gradient-boosted decision trees-based approaches for biomarker-Candidate discovery in multiomics data. Freiburg: Universität

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.