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This presentation from the BioMIBLab Summer 2006 Workshop is an analysis bioinformatics tool.


Algorithm for the Reconstruction of Accurate Cellular Networks

Designed to help understand mammalian normal cell physiology and complex pathologic phenotypes through elucidating gene transcriptional regulatory networks.


  • Algorithm is robust enough for its application in other network reconstruction problems in biology and the social and engineering fields.
  • Pairwise interaction model - higher-order potential interactions will not be accounted for (ARACNE’s algorithm will open 3-gene loops).
  • A two-gene interaction will be detected iff there are no alternate paths.
  • To keep three-gene loops, modify tolerance for edge-removal by introducing tolerance parameter, .
  • ARACNE’s performance deteriorates as local (true) network topology deviates from a tree (tight loops may be a problem).
  • ARACNE achieved high precision and substantial recall even for few data points when compared to BN and RN (synthetic data).
  • ARACNE cannot predict the orientation of the edges of the networks.
  • The algorithm is suited for more complex (mammalian) networks.

Full Presentation

ARACNE PowerPoint Presentation


"ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context," Margolin, Nemenman, et al.

"Cluster analysis and display of genome-wide expression patterns," Eisen, Spellman, et al.

"Conditional Network Analysis Identifies Candidate Regulator Genes in Human B cells," Wang, Banerjee, et al.

"Information theory, multivariate dependence, and genetic network inference," Nemenman.

"On The Reconstruction of Interaction Networks with Applications to Transcriptional Regulation," Margolin, Nemenman, et al.

"Reverse engineering of regulatory networks in human B cells," Basso, Margolin, et al.


ARACNE Homepage