FacPad specifications

Information


Unique identifier OMICS_15573
Name FacPad
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 3.0
Stability Stable
Requirements
Rlab, MASS
Source code URL https://cran.r-project.org/src/contrib/FacPad_3.0.tar.gz
Maintained No

Versioning


No version available

Maintainer


This tool is not available anymore.

Publication for FacPad

FacPad citations

 (2)
library_books

Identifying drug pathway association pairs based on L2,1 integrative penalized matrix decomposition

2017
BMC Syst Biol
PMCID: 5770056
PMID: 29297378
DOI: 10.1186/s12918-017-0480-7

[…] ways. But the GSEA method does not consider the known pathway information, its identification precision is poor []. In order to improve the identification precision and use the prior information, the FacPad method is proposed to predict drug-pathway associations, and it build a sparse Bayesian factor analysis model to infer pathway responsive for drug treatments []. In order to further improve the […]

library_books

Identifying drug pathway association pairs based on L1L2,1 integrative penalized matrix decomposition

2017
Oncotarget
PMCID: 5564627
PMID: 28624800
DOI: 10.18632/oncotarget.18254

[…] the drug effects into the global physiological environment account []. These existing computational methods for identifying drug targets include the Gene Set Enrichment Analysis (GSEA) method [], the FacPad method [], the iFad method [] and the iPaD method [], etc. The GSEA method has several disadvantages. Firstly, for every paired drug-pathway association, calculation must be done once at every […]

FacPad institution(s)
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA
FacPad funding source(s)
This work was supported by National Institutes of Health GM59507 and NIH R21-GM084008 and NSF DMS 1106738.

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