An Activation Force-based Affinity Measure for Analyzing Complex Networks

An Activation Force-based Affinity Measure for Analyzing Complex Networks

Affinity measure is a key factor that determines the quality of the analysis of a complex network. Here, we introduce a type of statistics, activation forces, to weight the links of a complex network and thereby develop a desired affinity measure. We show that the approach is superior in facilitatin...

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Main Authors: Guo, Jun, Guo, Hanliang, Wang, Zhanyi
Format: Online
Language:English
Published: Nature Publishing Group 2011
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3216595/
id pubmed-3216595
recordtype oai_dc
spelling pubmed-32165952011-12-22 An Activation Force-based Affinity Measure for Analyzing Complex Networks Guo, Jun Guo, Hanliang Wang, Zhanyi Article Affinity measure is a key factor that determines the quality of the analysis of a complex network. Here, we introduce a type of statistics, activation forces, to weight the links of a complex network and thereby develop a desired affinity measure. We show that the approach is superior in facilitating the analysis through experiments on a large-scale word network and a protein-protein interaction (PPI) network consisting of ∼5,000 human proteins. The experiment on the word network verifies that the measured word affinities are highly consistent with human knowledge. Further, the experiment on the PPI network verifies the measure and presents a general method for the identification of functionally similar proteins based on PPIs. Most strikingly, we find an affinity network that compactly connects the cancer-associated proteins to each other, which may reveal novel information for cancer study; this includes likely protein interactions and key proteins in cancer-related signal transduction pathways. Nature Publishing Group 2011-10-12 /pmc/articles/PMC3216595/ /pubmed/22355630 http://dx.doi.org/10.1038/srep00113 Text en Copyright © 2011, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Guo, Jun
Guo, Hanliang
Wang, Zhanyi
spellingShingle Guo, Jun
Guo, Hanliang
Wang, Zhanyi
An Activation Force-based Affinity Measure for Analyzing Complex Networks
author_facet Guo, Jun
Guo, Hanliang
Wang, Zhanyi
author_sort Guo, Jun
title An Activation Force-based Affinity Measure for Analyzing Complex Networks
title_short An Activation Force-based Affinity Measure for Analyzing Complex Networks
title_full An Activation Force-based Affinity Measure for Analyzing Complex Networks
title_fullStr An Activation Force-based Affinity Measure for Analyzing Complex Networks
title_full_unstemmed An Activation Force-based Affinity Measure for Analyzing Complex Networks
title_sort activation force-based affinity measure for analyzing complex networks
description Affinity measure is a key factor that determines the quality of the analysis of a complex network. Here, we introduce a type of statistics, activation forces, to weight the links of a complex network and thereby develop a desired affinity measure. We show that the approach is superior in facilitating the analysis through experiments on a large-scale word network and a protein-protein interaction (PPI) network consisting of ∼5,000 human proteins. The experiment on the word network verifies that the measured word affinities are highly consistent with human knowledge. Further, the experiment on the PPI network verifies the measure and presents a general method for the identification of functionally similar proteins based on PPIs. Most strikingly, we find an affinity network that compactly connects the cancer-associated proteins to each other, which may reveal novel information for cancer study; this includes likely protein interactions and key proteins in cancer-related signal transduction pathways.
publisher Nature Publishing Group
publishDate 2011
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3216595/
_version_ 1611487903134777344

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