predictionet specifications

Information


Unique identifier OMICS_10585
Name predictionet
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 1.26.0
Stability Stable
Requirements
knitr, MASS, RBGL, igraph, penalized, catnet, network, minet
Maintained Yes

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Documentation


Maintainer


  • person_outline Benjamin Haibe-Kains <>

Publication for predictionet

predictionet in publications

 (3)
PMCID: 5708769
PMID: 29190674
DOI: 10.1371/journal.pone.0188897

[…] prior networks were generated in metacore (genego, http://thomsonreuters.com/en/products-services/pharma-life-sciences/pharmaceuti-calresearch/metacore.html) and combined with text mining results (predictionet; http://www.bioconductor.org/packages/devel/bioc/html/predictionet.html). structural feedback loops were removed and, prior to performing the bayesian network analysis, z-score […]

PMCID: 4640223
PMID: 26553024
DOI: 10.1186/s12918-015-0226-3

[…] was principally used to generate the prior network along with thorough hand curation. the resultant seed network was then combined with the results obtained from ‘predictionet’ (http://www.bioconductor.org/packages/devel/bioc/html/predictionet.html), a text mining web application which retrieves gene interactions reported in the literature by focusing […]

PMCID: 4067568
PMID: 25009552
DOI: 10.3389/fgene.2014.00177

[…] in combination with three different genomic data sets., we use the inference procedure introduced in haibe-kains et al. (,) which is a two-step procedure implemented in the r/bioconductor package predictionet. the first step is a feature selection step based on the minimum redundancy, maximum relevance (mrmr, ding and peng, ; meyer et al., ) criterion whose robustness is improved […]


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predictionet institution(s)
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium; Ontario Cancer Institute, Princess Margaret Hospital/UHN, and the Campbell Family Institute for Cancer Research, University of Toronto, Toronto, ON, Canada; Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Entagen, Newburyport, MA, USA

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