Skip to main content
Fig. 1 | BMC Bioinformatics

Fig. 1

From: Combining learning and constraints for genome-wide protein annotation

Fig. 1

Depiction of the Ocelot decision making process. Above: predicted protein–protein interaction network, circles are proteins and lines represent physical interactions. Below: GO taxonomy, boxes are terms and arrows are IsA relations. Predicted annotations for proteins p1 and p2 (black): p1 is annotated with terms f1,f4,f5 and p2 with f2,f4. The functional predictions are driven by the similarity between p1 and p2, and by consistency with respect to the GO taxonomy (e.g. f1 entails either f3 or f4,f2 entails f4, etc.). The interaction predictions are driven by similarity between protein pairs (i.e. (p1,p2) against all other pairs) and are mutually constrained by the functional ones. For instance, since p1 and p2 do interact, OCELOT aims at predicting at least one shared term at each level of the GO, e.g. f4 at the middle level. These constraints are not hard, and can be violated if doing so provides a better joint prediction. As an example, p1 is annotated with f1 and p2 with f2. Please see the text for the details

Back to article page