Research Paper Volume 16, Issue 1 pp 153—168
Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning
Figure 6. Machine learning screens biomarkers for diagnosing OA. (A, B) The LASSO regression revealed that the number of genes corresponding to the lowest point of the curve (n = 6) is best suited for the diagnosis of OA. (C, D) Random Forest algorithm showed errors in OA; each gene is ranked according to its importance score. (E) The Venn diagram depicts the intersection genes of LASSO and RF results.