Figure 2

Overview of the performance of submitted and baseline algorithms across the four sub-challenges. Submissions were assessed by Z-scores combining RMSD, concordance index and Pearson’s correlation (see online methods for details on validation and testing). Performance was also compared to two baseline algorithms which were based on the top performing prediction algorithms submitted to the 2012 ALS prediction challenge, adapted to the requirements of the new challenge (see Supplementary material part 4). Grey boxes denote the performance of the best-performing baseline algorithm (left and right boundary of the box represent intervals of its performance ± the bootstrapped standard deviation). Teams that achieved the top three scores in any sub-challenge are indicated by colored symbols and shown by the same symbol in all sub-challenges. The BT score (right side of the figure) denotes the percentage of bootstrap samples where the top ranking team outperformed the second ranking team. The underlying method is indicated (RF = random forest, GBM = generalized boosting model, Cox = Cox model, GR = Gaussian regression). Submissions based on random forests, the most frequently used method, are denoted by symbols with dashed outlines.