Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution.

MLA

Agten, Annelies, et al. “Machine Learning Approach for the Prediction of the Number of Sulphur Atoms in Peptides Using the Theoretical Aggregated Isotope Distribution.” Rapid Communications in Mass Spectrometry: RCM, vol. 37, no. 9, May 2023, pp. 1–11. EBSCOhost, https://doi.org/10.1002/rcm.9480.

APA

Agten, A., Claesen, J., Burzykowski, T., & Valkenborg, D. (2023). Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution. Rapid Communications in Mass Spectrometry: RCM, 37(9), 1–11. https://doi.org/10.1002/rcm.9480

Chicago

Agten, Annelies, Jurgen Claesen, Tomasz Burzykowski, and Dirk Valkenborg. 2023. “Machine Learning Approach for the Prediction of the Number of Sulphur Atoms in Peptides Using the Theoretical Aggregated Isotope Distribution.” Rapid Communications in Mass Spectrometry: RCM 37 (9): 1–11. doi:10.1002/rcm.9480.