  Predicting protein interactions is one of the more interesting challenges of
the post-genomic era. Many algorithms address this problem as a binary
classification problem: given two proteins represented as two vectors of
features, predict if they interact or not. Importantly however, computational
predictions are only one component of a larger framework for identifying PPI.
The most promising candidate pairs can be validated experimentally by testing
if they physical bind to each other. Since these experiments are more costly
and error prone, the computational predictions serve as a filter, aimed to
produce a small number of highly promising candidates. Here we propose to
address this problem as a ranking problem: given a network with known
interactions, rank all unknown pairs based on the likelihood of their
interactions.
  In this paper we propose a ranking algorithm that trains multiple
inter-connected models using a passive aggressive on-line approach. We show
good results predicting protein-protein interactions for post synaptic density
PPI network. We compare the precision of the ranking algorithm with local
classifiers and classic support vector machine. Though the ranking algorithm
outperforms the classic SVM classification, its performance is inferior
compared to the local supervised method.
