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Predicting protein interactions is one of the more interesting challenges ofthe post-genomic era. Many algorithms address this problem as a binaryclassification problem: given two proteins represented as two vectors offeatures, predict if they interact or not. Importantly however, computationalpredictions are only one component of a larger framework for identifying PPI.The most promising candidate pairs can be validated experimentally by testingif they physical bind to each other. Since these experiments are more costlyand error prone, the computational predictions serve as a filter, aimed toproduce a small number of highly promising candidates. Here we propose toaddress this problem as a ranking problem: given a network with knowninteractions, rank all unknown pairs based on the likelihood of theirinteractions.  In this paper we propose a ranking algorithm that trains multipleinter-connected models using a passive aggressive on-line approach. We showgood results predicting protein-protein interactions for post synaptic densityPPI network. We compare the precision of the ranking algorithm with localclassifiers and classic support vector machine. Though the ranking algorithmoutperforms the classic SVM classification, its performance is inferiorcompared to the local supervised method.

COLoR - Coordinated On-Line Rankers for Network Reconstruction