Package: FunNet
Version: 1.00-1
Date: 2008-05-05
Title: Functional Analysis of Gene Co-expression Networks
Author: Corneliu Henegar <corneliu@henegar.info>
Maintainer: Corneliu Henegar <corneliu@henegar.info>
Depends: R (>= 2.4.0), ade4, cluster, Hmisc, nlme, sna, Cairo
Description: FunNet is an integrative tool for analyzing gene co-expression networks from
microarray expression data. The analytic model implemented in this library
involves two abstraction layers: transcriptional and functional (biological roles).
A functional profiling technique using Gene Ontology & KEGG annotations is
applied to extract a list of relevant biological themes from
microarray expression profiling data. Afterwards multiple-instance
representations are built to relate significant themes to their
transcriptional instances (i.e. the two layers of the model). An adapted
non-linear dynamical system model is used to quantify the proximity of relevant
genomic themes based on the similarity of the expression profiles of their gene instances.
Eventually an unsupervised multiple-instance clustering procedure, relying on
the two abstraction layers, is used to identify the structure of the co-expression
network composed from modules of functionally related transcripts. Functional
and transcriptional maps of the co-expression network are provided separately
together with detailed information on the network centrality of related transcripts
and genomic themes.
License: GPL (>= 2)
URL: http://corneliu.henegar.info/FunNet.htm,
http://www.geneontology.org/GO.tools.microarray.shtml#funnet,
http://www.funnet.info,
http://www.funnet.ws
Packaged: Mon May  5 14:10:48 2008; root

Package: FunNet
Version: 1.00-10
Date: 2010-08-23
Title: Integrative Functional Analysis of Transcriptional Networks
Author: Corneliu Henegar <corneliu@henegar.info>
Maintainer: Corneliu Henegar <corneliu@henegar.info>
Depends: R (>= 2.6.0), ade4, cluster, Hmisc, nlme, sna, Cairo
Description: FunNet is an integrative tool for analyzing gene
co-expression networks built from microarray expression data.
The analytic model implemented in this library involves two
abstraction layers: transcriptional and functional (biological
roles). A functional profiling technique using Gene Ontology &
KEGG annotations is applied to extract a list of relevant
biological themes from microarray expression profiling data.
Afterwards multiple-instance representations are built to
relate significant themes to their transcriptional instances
(i.e. the two layers of the model). An adapted non-linear
dynamical system model is used to quantify the proximity of
relevant genomic themes based on the similarity of the
expression profiles of their gene instances. Eventually an
unsupervised multiple-instance clustering procedure, relying on
the two abstraction layers, is used to identify the structure
of the co-expression network composed from modules of
functionally related transcripts. Functional and
transcriptional maps of the co-expression network are provided
separately together with detailed information on the network
centrality of related transcripts and genomic themes.
License: GPL (>= 2)
URL: http://corneliu.henegar.info/FunNet.htm,
http://www.geneontology.org/GO.tools.microarray.shtml#funnet,
http://www.funnet.info, http://www.funnet.ws
Packaged: 2010-08-23 17:36:59 UTC; CH
Repository: CRAN
Date/Publication: 2010-08-25 09:37:12