Figure 2

Visualization of the proposed method. Visualization of the proposed method. (a) Kernel preparation at the three levels. A kernel is derived for each input feature (Left); the resulting matrices are summed up to obtain a per-object kernel (Middle), which is transformed into a pairwise kernel using Eq 1. Here N _p (N _d , N _r ) is the number of individual proteins (respectively domains, residues) in the level, while N _{p p} (N _{d d} , N _{r r} ) is the number of protein (respectively domain, residue) interactions in the dataset. (b) Instantiation of all predicates (Table 1) over a pair of proteins p’ and p’ and their parts. Circles represent proteins, domains and residues. Dotted lines indicate a parent-child relationship between objects, representing the parentpd and parentdr predicates. Solid lines link pairs of bound objects, i.e. objects for which the boundp, boundd or boundr predicates are true. (c) Visualization of the experimental pipeline. Given the pairwise kernels, the set of rules (Table 2), a set of example interactions, and a description of the protein-domain-residue hierarchy, SBR finds a prediction for the query predicates consistent with the rules.