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ARACNE specifications

Information


Unique identifier OMICS_01851
Name ARACNE
Alternative names Algorithm for the Reconstruction of Accurate Cellular Networks, ARACNe-AP
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, Java
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainers


  • person_outline Artemis G. Hatzigeorgiou
  • person_outline Andrea Califano

Additional information


https://sourceforge.net/projects/aracne-ap/

Publications for Algorithm for the Reconstruction of Accurate Cellular Networks

ARACNE citations

 (162)
library_books

Time resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3

2018
BMC Biol
PMCID: 5937035
PMID: 29730990
DOI: 10.1186/s12915-018-0518-3

[…] To model the dynamics of the system, we reconstructed two gene networks using the log2(FPKM) values from ‘Early’ and ‘Late’ samples as indicated in Table and only for the HEGs.We used ARACNe [] to infer edges between the hubs and the expressed genes. Hubs were defined as the TFs that resulted differentially expressed at the gene (DEG) and protein (DEP) levels. In detail, we firstly […]

library_books

BTNET : boosted tree based gene regulatory network inference algorithm using time course measurement data

2018
BMC Syst Biol
PMCID: 5861501
PMID: 29560827
DOI: 10.1186/s12918-018-0547-0

[…] d, computes regulatory interaction scores using posterior probabilities obtained by BMA [].In contrast, model-free methods compute the degree of regulation based on information-theoretic criteria. TD-ARACNE [] obtains time-delayed dependency between two genes by mutual information. Similarly, time-delayed ND [] extracts dependencies based on cross-correlation instead and filters the indirect depen […]

library_books

From correlation to causation: analysis of metabolomics data using systems biology approaches

2018
Metabolomics
PMCID: 5829120
PMID: 29503602
DOI: 10.1007/s11306-018-1335-y

[…] al. () proposed a wisdom of crowd approach (Marbach et al. ) to define urine metabolite association networks in healthy subjects by considering the consensus obtained from four different approaches (ARACNE, CLR, PCLR and Pearson’s correlations) and deeming relevant only associations inferred by three or more methods. They modelled the subject-specific networks through a statistical mechanics appr […]

library_books

Candidate genes in gastric cancer identified by constructing a weighted gene co expression network

2018
PeerJ
PMCID: 5937478
PMID: 29740513
DOI: 10.7717/peerj.4692

[…] hat WGCNA outperforms many other methods in constructing the global network structure () and can safely replace mutual information networks based on non-linear gene expression associations () such as ARACNE (). Therefore, it is a robust method to understand gene expression information and has been widely and successfully applied in various biological contexts (; ; ).However, to our knowledge, ther […]

library_books

Systems epigenomics inference of transcription factor activity implicates aryl hydrocarbon receptor inactivation as a key event in lung cancer development

2017
Genome Biol
PMCID: 5738803
PMID: 29262847
DOI: 10.1186/s13059-017-1366-0

[…] ds such activated targets (Additional file : Table S1). We refer to this resulting bi-partite TF-target network as “LungNet.”Fig. 2 Importantly, we point out that (not unlike other algorithms such as ARACNE []) the predicted targets may not be direct binding targets of the TF, but could equally well represent indirect downstream targets which faithfully measure upstream TF binding activity. To inv […]

library_books

Prophetic Granger Causality to infer gene regulatory networks

2017
PLoS One
PMCID: 5718405
PMID: 29211761
DOI: 10.1371/journal.pone.0170340

[…] ed a conditional mutual information measure to prune down from a fully connected graph by eliminating nodes with mutual information explained by intermediaries very much like the previously published ARACNe method []. Nair et al. (2015) [] combine a Bayes net framework with additional topological node degree constraints that mimic observed biological networks, thereby reducing the complexity of th […]

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ARACNE institution(s)
Department of Biomedical Informatics, Columbia University, New York, NY, USA; Joint Centers for Systems Biology, Columbia University, New York, NY, USA; Institute for Cancer Genetics, Columbia University, New York, NY, USA; Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA; IBM T.J. Watson Research Center, Yorktown Heights, NY; USA
ARACNE funding source(s)
Supported by the NCI (1R01CA109755-01A1) and the NIAID (1R01AI066116-01), the NLM Medical Informatics Research Training Program (5 T15 LM007079-13).

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