Subnetworks and genes predict the age of fer-15 worms. Modular subnetworks are shown in green, regular subnetworks in blue, and gene sets in gray. This figure shows the best-performing type of modular subnetworks, regular subnetworks, and genes at each feature level. For modular subnetworks, this is type m3 at every feature level; for regular subnetworks, type r3 at 5 and 10 features, r2 at 25 features, and r4 at 50 features; for genes, g2 at all feature levels. Support vector regression algorithms using 5, 10, 25, or 50 features were trained to predict age on the data from Golden et al.  and tested on Budovskaya et al. . For each size of feature set, 1,000 different support vector regression learners were computed; curves show their median performance (quantified using the squared correlation coefficient (SCC) between true and predicted age in the bottom panel), and error bars indicate the 95% confidence intervals for the medians (calculated using a bootstrap estimate).