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ARACNE | An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context


An algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

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ARACNE classification

ARACNE specifications

Unique identifier:
Software type:
Restrictions to use:
Programming languages:
C++, Java
Algorithm for the Reconstruction of Accurate Cellular Networks
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:

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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

Funding source(s)

This work was 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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