Figure 3.

Predictability of seven example diseases evaluated by ROC curves in disease-centric assessment. Prediction performance for individual diseases is measured by the true positive rate (sensitivity) versus false positive rate (1 - specificity). In particular, for each given disease, each gene in the network is ranked based on the disease association score (Si; Equation 4). The Si for each known disease (seed) gene is computed using leave-one-out cross-validation, based on its connectivity to other seeds. Next the performance for each disease is assessed by calculating the sensitivity (True positives/(True positives + False negatives)) and 1 - specificity (False positives/(True negatives + False positives)) at different Si cutoffs. Here True positives is the number of seed genes above the Si cutoff, False positives is the number of non-seed genes above the cutoff, True negatives is the number of non-seed genes below the cutoff, and False negatives is the number of seed genes below the cutoff. Random prediction performance is indicated by the diagonal.

Linghu et al. Genome Biology 2009 10:R91   doi:10.1186/gb-2009-10-9-r91
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