Literature DB >> 21075747

QuACN: an R package for analyzing complex biological networks quantitatively.

Laurin A J Mueller1, Karl G Kugler, Andreas Dander, Armin Graber, Matthias Dehmer.   

Abstract

MOTIVATION: Network-based representations of biological data have become an important way to analyze high-throughput data. To interpret the large amount of data that is produced by different high-throughput technologies, networks offer multifaceted aspects to analyze the data. As networks represent biological relationships within their structure, it turned out to be fruitful to analyze their topology. Therefore, we developed a freely available, open source R-package called Quantitative Analysis of Complex Networks (QuACN) to meet this challenge. QuACN contains different, information-theoretic and non-information-theoretic, topological network descriptors to analyze, classify and compare biological networks. AVAILABILITY: QuACN is freely available under LGPL via CRAN (http://cran.r-project.org/web/packages/QuACN/).

Entities:  

Mesh:

Year:  2010        PMID: 21075747     DOI: 10.1093/bioinformatics/btq606

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  20 in total

1.  Topological analysis and interactive visualization of biological networks and protein structures.

Authors:  Nadezhda T Doncheva; Yassen Assenov; Francisco S Domingues; Mario Albrecht
Journal:  Nat Protoc       Date:  2012-03-15       Impact factor: 13.491

2.  A network analysis of crab metamorphosis and the hypothesis of development as a process of unfolding of an intensive complexity.

Authors:  Agustín Ostachuk
Journal:  Sci Rep       Date:  2021-05-05       Impact factor: 4.379

3.  Exploring statistical and population aspects of network complexity.

Authors:  Frank Emmert-Streib; Matthias Dehmer
Journal:  PLoS One       Date:  2012-05-08       Impact factor: 3.240

4.  Integrative network biology: graph prototyping for co-expression cancer networks.

Authors:  Karl G Kugler; Laurin A J Mueller; Armin Graber; Matthias Dehmer
Journal:  PLoS One       Date:  2011-07-29       Impact factor: 3.240

5.  A network-based approach to classify the three domains of life.

Authors:  Laurin A J Mueller; Karl G Kugler; Michael Netzer; Armin Graber; Matthias Dehmer
Journal:  Biol Direct       Date:  2011-10-13       Impact factor: 4.540

6.  Information indices with high discriminative power for graphs.

Authors:  Matthias Dehmer; Martin Grabner; Kurt Varmuza
Journal:  PLoS One       Date:  2012-02-29       Impact factor: 3.240

7.  Structural measures for network biology using QuACN.

Authors:  Laurin A J Mueller; Karl G Kugler; Armin Graber; Frank Emmert-Streib; Matthias Dehmer
Journal:  BMC Bioinformatics       Date:  2011-12-24       Impact factor: 3.307

8.  Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers.

Authors:  Michael Netzer; Klaus M Weinberger; Michael Handler; Michael Seger; Xiaocong Fang; Karl G Kugler; Armin Graber; Christian Baumgartner
Journal:  J Clin Bioinforma       Date:  2011-12-19

9.  RMol: a toolset for transforming SD/Molfile structure information into R objects.

Authors:  Martin Grabner; Kurt Varmuza; Matthias Dehmer
Journal:  Source Code Biol Med       Date:  2012-11-14

10.  Navigating traditional chinese medicine network pharmacology and computational tools.

Authors:  Ming Yang; Jia-Lei Chen; Li-Wen Xu; Guang Ji
Journal:  Evid Based Complement Alternat Med       Date:  2013-07-31       Impact factor: 2.629

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