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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

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

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