Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/33804
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dc.contributor.authorPavlopoulos, G.A.-
dc.contributor.authorHooper, S.D.-
dc.contributor.authorSifrim, A.-
dc.contributor.authorSchneider, R.-
dc.contributor.authorAERTS, Jan-
dc.date.accessioned2021-04-02T08:20:37Z-
dc.date.available2021-04-02T08:20:37Z-
dc.date.issued2011-
dc.date.submitted2021-03-22T13:33:55Z-
dc.identifier.citationBMC research notes, 4 (1) (Art N° 384)-
dc.identifier.isbn17560500-
dc.identifier.urihttp://hdl.handle.net/1942/33804-
dc.description.abstractBackground: Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their analysis, and their visualization are today important challenges in life sciences. While a great variety of visualization tools that try to address most of these challenges already exists, only few of them succeed to bridge the gap between visualization and network analysis. Findings: Medusa is a powerful tool for visualization and clustering analysis of large-scale biological networks. It is highly interactive and it supports weighted and unweighted multi-edged directed and undirected graphs. It combines a variety of layouts and clustering methods for comprehensive views and advanced data analysis. Its main purpose is to integrate visualization and analysis of heterogeneous data from different sources into a single network.-
dc.language.isoen-
dc.publisher-
dc.rights2011 Pavlopoulos et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.titleMedusa: A tool for exploring and clustering biological networks-
dc.typeJournal Contribution-
dc.identifier.issue1-
dc.identifier.volume4-
local.bibliographicCitation.jcatA2-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr384-
dc.identifier.doi10.1186/1756-0500-4-384-
dc.identifier.scopus2-s2.0-80053463904-
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-80053463904&partnerID=MN8TOARS-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.identifier.eissn-
dc.identifier.eissn-
local.provider.typeOrcid-
local.uhasselt.uhpubno-
local.uhasselt.internationalyes-
item.fulltextWith Fulltext-
item.accessRightsClosed Access-
item.fullcitationPavlopoulos, G.A.; Hooper, S.D.; Sifrim, A.; Schneider, R. & AERTS, Jan (2011) Medusa: A tool for exploring and clustering biological networks. In: BMC research notes, 4 (1) (Art N° 384).-
item.contributorPavlopoulos, G.A.-
item.contributorHooper, S.D.-
item.contributorSifrim, A.-
item.contributorSchneider, R.-
item.contributorAERTS, Jan-
crisitem.journal.issn1756-0500-
crisitem.journal.eissn1756-0500-
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