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The dataset is split randomly into training (70%) and testing (30%) sets, and three Cox models are fitted to the training data: (a) a model with only selected clinical covariates, (b) a model with only anti-profile scores, and (c) a model with both clinical covariates and the anti-profile scores. For each model, the mean survival at time t is calculated for training set patients surviving and not-surviving at that [http://www.selleckchem.com/products/jq1.html Epigenetic Reader Domain inhibitor] time point. For each patient in the testing set, the predicted survival probability at time t is compared to the surviving group mean and not-surviving group mean, and the closest group is chosen to predict whether the patient will survive or not. These predictions are compared to [https://en.wikipedia.org/wiki/E-64 E-64] actual survival to calculate an accuracy rate (patients censored by the time t are not used for the calculation). This process is repeated for a 100 training and testing subsets created from the main dataset, and the distribution of accuracy values was plotted. For the second breast cancer dataset,31 a Cox model fitted with the pathological stage proved to be less accurate than a model fitted with the anti-profile score (Fig. 5A) for predicting patient death at 5 years. The mean accuracy level for the model fitted with the pathological stage was 0.619, for a model fitted with the anti-profile score was 0.726, and for a model fitted with both covariates was 0.655. A Wilcoxon test between the results from the first and third models yielded P-value [http://www.selleckchem.com/products/r428.html R428 mouse] Anti-profiles applied to Cox proportional hazard models for survival prediction: Cox proportional hazard models with significant clinical covariates and anti-profiles were used to predict patient survival at 5 years for the second breast caner dataset ... We used a similar experiment on the lung and colon-cancer datasets mentioned above, but found that adding anti-profile scores to survival models, including significant clinical covariates, did not improve their performance significantly (Supplementary Fig. 12). Conclusions Our aim has been to develop a robust and stable approach for classification of tumor samples. We have demonstrated that the anti-profile scoring method, which was initially applied for classification between tumor and normal samples, can be extended to classification between tumor samples as well.