MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.
MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.
Authors: Christopher L Poirel; Ahsanur Rahman; Richard R Rodrigues; Arjun Krishnan; Jacqueline R Addesa; T M Murali Journal: Bioinformatics Date: 2013-01-12 Impact factor: 6.937
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Authors: Victoria Sandler; Anna C Reisetter; James R Bain; Michael J Muehlbauer; Michael Nodzenski; Robert D Stevens; Olga Ilkayeva; Lynn P Lowe; Boyd E Metzger; Christopher B Newgard; Denise M Scholtens; William L Lowe Journal: Diabetologia Date: 2016-12-16 Impact factor: 10.122
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