ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
Open accessAbstract
BACKGROUND: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel 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. RESULTS: We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. CONCLUSION: 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 removes indirect connections between genes, leaving only those connections that are expected to be regulatory.
“ARACNE removes indirect connections between genes (i.e. partners of a gene that have a stronger correlation with each other than with the gene itself), leaving only those connections that are expected to be regulatory.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [23]
The Algorithm section says ARACNE removes the vast majority of indirect candidate interactions, and the Data Processing Inequality section specifies that it removes the weakest edge in a gene triplet based on mutual information—not correlation. The Background says the remaining dependencies are likely to identify direct regulatory interactions, but also cautions that other types of interactions may be identified. Thus the claim captures the method's aim but overstates what the retained connections represent. (LLM, conf=0.86)
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- Authors
- Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., & Califano, A.
- Affiliations
- Columbia University (US)
- IBM Research - Thomas J. Watson Research Center (US)
- Corresponding author
- Califano, Andrea
- Venue
- BMC Bioinformatics
- Published
- 2006-03
- Volume
- 7
- Issue
- S1
- Pages
- S7-S7
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC1810318
- OpenAlex
- W2163296115
- Cited by
- 2,420
- References
- 21
- Citations by year
- 2026: 89 · 2025: 135 · 2024: 131 · 2023: 154 · 2022: 126 · 2021: 156 · 2020: 171 · 2019: 142 · 2018: 154 · 2017: 166 · 2016: 142 · 2015: 141 · 2014: 124 · 2013: 129 · 2012: 120
- FWCI
- 21.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- arXiv (Cornell University)
- Funders
- National Cancer Institute (R01CA109755)
- National Institute of Allergy and Infectious Diseases (R01AI066116)
- U.S. National Library of Medicine (T15LM007079)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene regulatory network reconstruction, ARACNE, mutual information, transcriptional regulatory networks, mammalian cells, human B cells, c-Myc, network loops, microarray expression profiles
- MeSH terms
- Proto-Oncogene Mas, Algorithms, Animals, B-Lymphocytes, Computer Simulation, Gene Expression Regulation, Humans, Phenotype, Software, Transcription, Genetic, Reproducibility of Results, Models, Statistical, Neural Networks, Computer, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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