Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms

<div>(a) The tuning parameter plot of the lasso Cox model. The <i>x</i>-axis represents log-transformed lambda values, and the <i>y</i>-axis represents the partial likelihood deviance. The vertical dashed line indicates the minimal partial likelihood deviance. (b) The time-dependent ROC curve of the lasso Cox model at 1 year (12 months), with an AUC of 0.825. (c) The time-dependent ROC curve of the lasso Cox model at 3 years (36 months), with an AUC of 0.821. (d) The time-dependent ROC curve of the lasso Cox model at 5 years (60 months), with an AUC of 0.815.</div>

Journal of Oncology

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Figure 4: Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms 

Figure 4 | Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms 