This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see predictionet.
Bioconductor version: 3.9
This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen.
Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush
Maintainer: Benjamin Haibe-Kains <bhaibeka at jimmy.harvard.edu>, Catharina Olsen <colsen at ulb.ac.be>
Citation (from within R,
enter citation("predictionet")
):
To install this package, start R (version "3.6") and enter:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("predictionet")
For older versions of R, please refer to the appropriate Bioconductor release.
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("predictionet")
R Script | predictionet | |
Reference Manual | ||
Text | README |
biocViews | GraphAndNetwork, NetworkInference, Software |
Version | 1.30.0 |
In Bioconductor since | BioC 2.9 (R-2.14) (8 years) |
License | Artistic-2.0 |
Depends | igraph, catnet |
Imports | penalized, RBGL, MASS |
LinkingTo | |
Suggests | network, minet, knitr |
SystemRequirements | |
Enhances | |
URL | http://compbio.dfci.harvard.edu http://www.ulb.ac.be/di/mlg |
Depends On Me | |
Imports Me | |
Suggests Me | |
Links To Me | |
Build Report |
Follow Installation instructions to use this package in your R session.
Source Package | predictionet_1.30.0.tar.gz |
Windows Binary | |
Mac OS X 10.11 (El Capitan) | predictionet_1.30.0.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/predictionet |
Source Repository (Developer Access) | git clone [email protected]:packages/predictionet |
Package Short Url | https://bioconductor.org/packages/predictionet/ |
Package Downloads Report | Download Stats |
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