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This is the development version of BioNet; for the stable release version, see BioNet.

Routines for the functional analysis of biological networks

Bioconductor version: Development (3.20)

This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork.

Author: Marcus Dittrich and Daniela Beisser

Maintainer: Marcus Dittrich <marcus.dittrich at>

Citation (from within R, enter citation("BioNet")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


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:

BioNet Tutorial PDF R Script
Reference Manual PDF


biocViews DataImport, DifferentialExpression, GeneExpression, GraphAndNetwork, Microarray, Network, NetworkEnrichment, Software
Version 1.65.0
In Bioconductor since BioC 2.7 (R-2.12) (13.5 years)
License GPL (>= 2)
Depends R (>= 2.10.0), graph, RBGL
Imports igraph (>= 1.0.1), AnnotationDbi, Biobase
System Requirements
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Suggests rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML
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Depends On Me
Imports Me gatom, SMITE
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Follow Installation instructions to use this package in your R session.

Source Package BioNet_1.65.0.tar.gz
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macOS Binary (x86_64) BioNet_1.65.0.tgz
macOS Binary (arm64) BioNet_1.65.0.tgz
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Source Repository (Developer Access) git clone
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