Protein function prediction based on neighborhood profiles

Abstract

The recent advent of high throughput methods has generated large amounts of protein interaction network (PIN) data. A significant number of proteins in such networks remain uncharacterized and predicting their function remains a major challenge. A number of existing techniques assume that proteins with similar functions are topologically close in the network. Our hypothesis is that the simultaneous activity of sometimes functionally diverse functional agents comprises higher level processes in different regions of the PIN. We propose a two-phase approach. First we extract the neighborhood profile of a protein using Random Walks with Restarts. We then employ a “chisquare method”, which assigns k functions to an uncharacterized protein, with the k largest chi-square scores. We applied our method on protein physical interaction data and protein complex data, which showed the later perform better. We performed leave-one-out validation to measure the accuracy of the predictions, revealing significant improvements over previous techniques.