Research Paper Volume 16, Issue 6 pp 5651—5675

Figure 3. Machine learning algorithms were leveraged to construct a predictive model for therapy-response CD14⁺ monocytes and explored potential indicators. (A) LASSO algorithm was adopted for filtering optimal feature genes. Only the 21 highly-conserved makers/DEGs for therapy-response CD14⁺ monocytes were adopted. (B) Coefficients of identified feature genes from LASSO algorithm was shown. (C) ROC analysis for the LASSO model. LASSO: Least absolute shrinkage and selection operator. (D) Machine learning algorithm for constructing a predictive model for therapy-response CD14⁺ monocytes. (E) ROC analysis for the machine learning model. (F) ROC analysis for the machine learning model in test cohort. The AUC value was 0.98. AUC: area under curve. (G) Intersection of genes identified by the LASSO algorithm with the highly conserved markers of the therapy-response CD14⁺ monocytes. Seven overlapped genes were delineated. (H) Protein-protein interaction network for identified genes. The network prediction was based upon an online web-server: GeneMANIA (http://www.genemania.org).