Protein function prediction based on neighborhood profiles
Date Issued
2009-09-28
Author(s)
Cingovska, Ivana
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.
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.
Subjects
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