dorsal/arxiv
View SchemaARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
| Authors | Adam A. Margolin, Ilya Nemenman, Katia Basso, Ulf Klein, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, Andrea Califano |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0410037 |
| URL | https://arxiv.org/abs/q-bio/0410037 |
| DOI | 10.1186/1471-2105-7-S1-S7 |
| Journal | BMC Bioinformatics 2006, 7(Suppl 1):S7 |
Abstract
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 c MYC proto-oncogene. We also study the effects of mis estimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.
{
"annotation_id": "6e39dbfa-51c7-4e9c-a7ae-65acfe1e65a6",
"date_created": "2026-03-02T18:01:31.744000Z",
"date_modified": "2026-03-02T18:01:31.744000Z",
"file_hash": "0466ac2505c266b7a85b184b2141551b7e515d064f1637447db2a608336563df",
"private": false,
"record": {
"abstract": "Background: Elucidating gene regulatory networks is crucial for understanding\nnormal cell physiology and complex pathologic phenotypes. Existing\ncomputational methods for the genome-wide ``reverse engineering\u0027\u0027 of such\nnetworks have been successful only for lower eukaryotes with simple genomes.\nHere we present ARACNE, a novel algorithm, using microarray expression\nprofiles, specifically designed to scale up to the complexity of regulatory\nnetworks in mammalian cells, yet general enough to address a wider range of\nnetwork deconvolution problems. This method uses an information theoretic\napproach to eliminate the majority of indirect interactions inferred by\nco-expression methods.\n Results: We prove that ARACNE reconstructs the network exactly\n(asymptotically) if the effect of loops in the network topology is negligible,\nand we show that the algorithm works well in practice, even in the presence of\nnumerous loops and complex topologies. We assess ARACNE\u0027s ability to\nreconstruct transcriptional regulatory networks using both a realistic\nsynthetic dataset and a microarray dataset from human B cells. On synthetic\ndatasets ARACNE achieves very low error rates and outperforms established\nmethods, such as Relevance Networks and Bayesian Networks. Application to the\ndeconvolution of genetic networks in human B cells demonstrates ARACNE\u0027s\nability to infer validated transcriptional targets of the c MYC proto-oncogene.\nWe also study the effects of mis estimation of mutual information on network\nreconstruction, and show that algorithms based on mutual information ranking\nare more resilient to estimation errors.",
"arxiv_id": "q-bio/0410037",
"authors": [
"Adam A. Margolin",
"Ilya Nemenman",
"Katia Basso",
"Ulf Klein",
"Chris Wiggins",
"Gustavo Stolovitzky",
"Riccardo Dalla Favera",
"Andrea Califano"
],
"categories": [
"q-bio.MN",
"q-bio.GN",
"q-bio.QM"
],
"doi": "10.1186/1471-2105-7-S1-S7",
"journal_ref": "BMC Bioinformatics 2006, 7(Suppl 1):S7",
"title": "ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context",
"url": "https://arxiv.org/abs/q-bio/0410037"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4501e792-5118-44c0-9c4d-0c11de831ea0",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}