Abdelhamid, S.S.; Scioscia, J.; Vodovotz, Y.; Wu, J.; Rosengart, A.; Sung, E.; Rahman, S.; Voinchet, R.; Bonaroti, J.; Li, S.;
et al. Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients. Metabolites 2022, 12, 774.
https://doi.org/10.3390/metabo12090774
AMA Style
Abdelhamid SS, Scioscia J, Vodovotz Y, Wu J, Rosengart A, Sung E, Rahman S, Voinchet R, Bonaroti J, Li S,
et al. Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients. Metabolites. 2022; 12(9):774.
https://doi.org/10.3390/metabo12090774
Chicago/Turabian Style
Abdelhamid, Sultan S., Jacob Scioscia, Yoram Vodovotz, Junru Wu, Anna Rosengart, Eunseo Sung, Syed Rahman, Robert Voinchet, Jillian Bonaroti, Shimena Li,
and et al. 2022. "Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients" Metabolites 12, no. 9: 774.
https://doi.org/10.3390/metabo12090774
APA Style
Abdelhamid, S. S., Scioscia, J., Vodovotz, Y., Wu, J., Rosengart, A., Sung, E., Rahman, S., Voinchet, R., Bonaroti, J., Li, S., Darby, J. L., Kar, U. K., Neal, M. D., Sperry, J., Das, J., & Billiar, T. R.
(2022). Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients. Metabolites, 12(9), 774.
https://doi.org/10.3390/metabo12090774