Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/34307
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dc.contributor.authorPavlopoulos, G.A.-
dc.contributor.authorSecrier, M.-
dc.contributor.authorMoschopoulos, C.N.-
dc.contributor.authorSoldatos, T.G.-
dc.contributor.authorKossida, S.-
dc.contributor.authorAERTS, Jan-
dc.contributor.authorSchneider, R.-
dc.contributor.authorBagos, P.G.-
dc.date.accessioned2021-06-21T09:48:24Z-
dc.date.available2021-06-21T09:48:24Z-
dc.date.issued2011-
dc.date.submitted2021-03-22T13:28:32Z-
dc.identifier.citationBioData Mining, 4 (1) (Art N° 10)-
dc.identifier.urihttp://hdl.handle.net/1942/34307-
dc.description.abstractUnderstanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.-
dc.language.isoen-
dc.publisher-
dc.publisher-
dc.subject.otherbiological network clustering analysis-
dc.subject.othergraph theory-
dc.subject.othernode ranking-
dc.titleUsing graph theory to analyze biological networks-
dc.typeJournal Contribution-
dc.identifier.issue1-
dc.identifier.volume4-
local.bibliographicCitation.jcatA1-
local.publisher.placeCAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND-
local.type.refereedRefereed-
local.type.specifiedReview-
local.bibliographicCitation.artnr10-
dc.identifier.doi10.1186/1756-0381-4-10-
dc.identifier.scopus2-s2.0-79955130092-
dc.identifier.isiWOS:000208761200010-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
local.provider.typeOrcid-
local.uhasselt.uhpubno-
item.contributorPavlopoulos, G.A.-
item.contributorSecrier, M.-
item.contributorMoschopoulos, C.N.-
item.contributorSoldatos, T.G.-
item.contributorKossida, S.-
item.contributorAERTS, Jan-
item.contributorSchneider, R.-
item.contributorBagos, P.G.-
item.fullcitationPavlopoulos, G.A.; Secrier, M.; Moschopoulos, C.N.; Soldatos, T.G.; Kossida, S.; AERTS, Jan; Schneider, R. & Bagos, P.G. (2011) Using graph theory to analyze biological networks. In: BioData Mining, 4 (1) (Art N° 10).-
item.accessRightsClosed Access-
item.fulltextNo Fulltext-
crisitem.journal.issn1756-0381-
crisitem.journal.eissn1756-0381-
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