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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks

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Camilo, Esther [1] ; Bovolenta, Luiz A. [1] ; Acencio, Marcio L. [1] ; Rybarczyk-Filho, Jose L. [1] ; Castro, Mauro A. A. [2] ; Moreira, Jose C. F. [2] ; Lemke, Ney [1]
Total Authors: 7
[1] UNESP Univ Estadual Paulista, Inst Biosci, Dept Phys & Biophys, Sao Paulo - Brazil
[2] Univ Fed Rio Grande do Sul, Inst Basic Sci & Hlth, Porto Alegre, RS - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Bioinformatics; v. 29, n. 19, p. 2505-2506, OCT 1 2013.
Web of Science Citations: 3

Network-level visualization of functional data is a key aspect of both analysis and understanding of biological systems. In a continuing effort to create clear and integrated visualizations that facilitate the gathering of novel biological insights despite the overwhelming complexity of data, we present here the GrAph LANdscape VisualizaTion (GALANT), a Cytoscape plugin that builds functional landscapes onto biological networks. By using GALANT, it is possible to project any type of numerical data onto a network to create a smoothed data map resembling the network layout. As a Cytoscape plugin, GALANT is further improved by the functionalities of Cytoscape, the popular bioinformatics package for biological network visualization and data integration. (AU)

FAPESP's process: 10/20684-3 - Development of machine learning approaches based on biological networks for prediction and determination of rules governing the emergence of phenotypes of interest
Grantee:Marcio Luis Acencio
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 12/13450-1 - Large scale exploratory analysis of Nile Tilapia's miRNA expression using bioinformatics tools
Grantee:Luiz Augusto Bovolenta
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 13/02018-4 - Machine learning for molecular systems biology (MLMSB) application on synthetic lethality, conditionally essential genes and cooperative transcription
Grantee:Ney Lemke
Support type: Regular Research Grants
FAPESP's process: 12/00741-8 - Prediction of Escherichia coli phenotypes through biological networks and machine learning
Grantee:Esther Camilo dos Reis
Support type: Scholarships in Brazil - Doctorate