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Figure 3 | Genome Biology

Figure 3

From: Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

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 (S i ; Equation 4). The S i 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 S i cutoffs. Here True positives is the number of seed genes above the S i 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.

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