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.
"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.
"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.