Contini, C.; Manconi, B.; Olianas, A.; Guadalupi, G.; Schirru, A.; Zorcolo, L.; Castagnola, M.; Messana, I.; Faa, G.; Diaz, G.;
et al. Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer. Cells 2024, 13, 1311.
https://doi.org/10.3390/cells13161311
AMA Style
Contini C, Manconi B, Olianas A, Guadalupi G, Schirru A, Zorcolo L, Castagnola M, Messana I, Faa G, Diaz G,
et al. Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer. Cells. 2024; 13(16):1311.
https://doi.org/10.3390/cells13161311
Chicago/Turabian Style
Contini, Cristina, Barbara Manconi, Alessandra Olianas, Giulia Guadalupi, Alessandra Schirru, Luigi Zorcolo, Massimo Castagnola, Irene Messana, Gavino Faa, Giacomo Diaz,
and et al. 2024. "Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer" Cells 13, no. 16: 1311.
https://doi.org/10.3390/cells13161311
APA Style
Contini, C., Manconi, B., Olianas, A., Guadalupi, G., Schirru, A., Zorcolo, L., Castagnola, M., Messana, I., Faa, G., Diaz, G., & Cabras, T.
(2024). Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer. Cells, 13(16), 1311.
https://doi.org/10.3390/cells13161311
