Geometric de-noising of protein-protein interaction networks.

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein...

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Autores principales:	Oleksii Kuchaiev, Marija Rasajski, Desmond J Higham, Natasa Przulj
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/253ba1dbac7846a6ba51b30939723b97
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spelling	oai:doaj.org-article:253ba1dbac7846a6ba51b30939723b972021-11-25T05:42:15ZGeometric de-noising of protein-protein interaction networks.1553-734X1553-735810.1371/journal.pcbi.1000454https://doaj.org/article/253ba1dbac7846a6ba51b30939723b972009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19662157/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.Oleksii KuchaievMarija RasajskiDesmond J HighamNatasa PrzuljPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000454 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Oleksii Kuchaiev Marija Rasajski Desmond J Higham Natasa Przulj Geometric de-noising of protein-protein interaction networks.
description	Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.
format	article
author	Oleksii Kuchaiev Marija Rasajski Desmond J Higham Natasa Przulj
author_facet	Oleksii Kuchaiev Marija Rasajski Desmond J Higham Natasa Przulj
author_sort	Oleksii Kuchaiev
title	Geometric de-noising of protein-protein interaction networks.
title_short	Geometric de-noising of protein-protein interaction networks.
title_full	Geometric de-noising of protein-protein interaction networks.
title_fullStr	Geometric de-noising of protein-protein interaction networks.
title_full_unstemmed	Geometric de-noising of protein-protein interaction networks.
title_sort	geometric de-noising of protein-protein interaction networks.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/253ba1dbac7846a6ba51b30939723b97
work_keys_str_mv	AT oleksiikuchaiev geometricdenoisingofproteinproteininteractionnetworks AT marijarasajski geometricdenoisingofproteinproteininteractionnetworks AT desmondjhigham geometricdenoisingofproteinproteininteractionnetworks AT natasaprzulj geometricdenoisingofproteinproteininteractionnetworks
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Geometric de-noising of protein-protein interaction networks.

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