Research Paper Volume 15, Issue 20 pp 11162—11183
Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer
Figure 1. Integrative machine learning analysis constructed a prognostic PI3K/Akt pathway related signature. (A) Potential biomarker identified by univariate cox analysis. (B) The C-index of 101 kinds prognostic models constructed by 10 machine learning algorithms in training and testing cohort. (C) The determination of the optimal λ was obtained when the partial likelihood deviance reached the minimum value, and further generated Lasso coefficients of the most useful prognostic genes. (D) Coefficients of 19 genes finally obtained in survivalSVM regression. The survival curve of ovarian cancer with high and low risk score in TCGA (E), GSE14764 (F), GSE26193 (G), GSE26172 (H), GSE63885 (I) and GSE140082 (J) cohort. Cluster analysis of ovarian cancer cases with high and low risk score in TCGA (K), GSE14764 (L) GSE26193 (M), GSE26172 (N), GSE63885(O) and GSE140082 (P) cohort by using tSNE method.