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Prediction of protein function using protein-protein interaction data

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5 Author(s)
Minghua Deng ; Dept. of Biol. Sci., Univ. of Southern California, Los Angeles, CA, USA ; Kui Zhang ; Mehta, S. ; Ting Chen
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Assigning functions to novel proteins is one of the most important problems in the post-genomic era. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database, using the protein-protein interaction data from the Munich Information Center for Protein Sequences. We show that our approach outperforms other available methods for function prediction based on protein interaction data.

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Bioinformatics Conference, 2002. Proceedings. IEEE Computer Society

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