Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI.
|Author||Matthew N. McCall <email@example.com>, Anthony Almudevar <Anthony_Alumudevar@urmc.rochester.edu>, David Burton <David_Burton@urmc.rochester.edu>, Harry Stern <firstname.lastname@example.org>|
|Bioconductor views||Bayesian CellBiology GraphAndNetwork Network Software|
|Maintainer||McCall N. Matthew <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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