Research Paper Advance Articles
Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach
Figure 4. Identification of melanoma-related key CRGs by machine learning approaches. (A) Feature CRG selection using SVM-RFE algorithm. (B) Importance ranking of CRGs using a random forest algorithm. The top 20 CRGs ranked by importance were selected as feature genes. (C) Selection of melanoma-associated feature genes using LASSO regression model. (D) Identification of melanoma-related key CRGs. The overlapping feature genes (CRGs) from the three machine learning approaches were defined as melanoma-related key CRGs.