Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. 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(YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS,
%0 Journal Article
%1 Deng2003Prediction
%A Deng, Minghua
%A Zhang, Kui
%A Mehta, Shipra
%A Chen, Ting
%A Sun, Fengzhu
%D 2003
%J Journal of Computational Biology
%K protein\_interaction, proteins networks biological-networks
%P 947--960
%T Prediction of protein function using protein-protein interaction data
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.3428
%V 10
%X Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. 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(YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS,
@article{Deng2003Prediction,
abstract = {{Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. 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(YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS,}},
added-at = {2019-06-10T14:53:09.000+0200},
author = {Deng, Minghua and Zhang, Kui and Mehta, Shipra and Chen, Ting and Sun, Fengzhu},
biburl = {https://www.bibsonomy.org/bibtex/2556783a8afb14f3d4798db942aa9d104/nonancourt},
citeulike-article-id = {3789159},
citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.3428},
comment = {Paper sent by Brendan},
interhash = {0426d1c7e576418ac2d938e963382282},
intrahash = {556783a8afb14f3d4798db942aa9d104},
journal = {Journal of Computational Biology},
keywords = {protein\_interaction, proteins networks biological-networks},
pages = {947--960},
posted-at = {2008-12-15 09:52:11},
priority = {2},
timestamp = {2019-07-31T13:50:37.000+0200},
title = {{Prediction of protein function using protein-protein interaction data}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.3428},
volume = 10,
year = 2003
}