Predicting protein function at the proteomic-scale is a key task in computational systems biology. High-throughput experimental methods have recently made available many protein interaction networks that need to be analyzed in order to provide insightinto the functional role of proteins in the organization of the cell. We propose here a new approach to computational function annotation of protein interaction maps based on a random walk algorithm. Our method exploits the whole topology of the networkaccording to the basic principles of a ranking algorithm for link analysis. We apply the proposed algorithm to analyze the yeast protein interaction network and show that it represents a valid alternative to other annotation techniques based on networkanalysis by comparing it with the effective majority vote algorithm.
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