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http://hdl.handle.net/1942/33804
Title: | Medusa: A tool for exploring and clustering biological networks | Authors: | Pavlopoulos, G.A. Hooper, S.D. Sifrim, A. Schneider, R. AERTS, Jan |
Issue Date: | 2011 | Publisher: | Source: | BMC research notes, 4 (1) (Art N° 384) | Abstract: | Background: 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. | Document URI: | http://hdl.handle.net/1942/33804 | Link to publication/dataset: | http://www.scopus.com/inward/record.url?eid=2-s2.0-80053463904&partnerID=MN8TOARS | ISBN: | 17560500 | ISSN: | 1756-0500 | e-ISSN: | 1756-0500 | DOI: | 10.1186/1756-0500-4-384 | Rights: | 2011 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. | Category: | A2 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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