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 insight into 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 network according 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 network analysis by comparing it with the effective majority vote algorithm

Protein function prediction from interaction networks using a random walk ranking algorithm

FRESCHI, VALERIO
2007-01-01

Abstract

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 insight into 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 network according 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 network analysis by comparing it with the effective majority vote algorithm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2504794
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