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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("geNetClassifier")
In most cases, you don't need to download the package archive at all.
geNetClassifier
classify diseases and build associated gene networks using gene expression profiles
Bioconductor version: 2.14
Comprehensive package to automatically train a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.
Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.
Maintainer: Sara Aibar <saibar at usal.es>
Citation (from within R,
enter citation("geNetClassifier")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("geNetClassifier")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("geNetClassifier")
| R Script | geNetClassifier-vignette | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Cancer, Classification, GeneExpression, Leukemia, Microarray, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>= 2) |
| Depends | R (>= 2.10.1), Biobase(>= 2.5.5), EBarrays, minet, methods |
| Imports | e1071, ipred, graphics |
| Suggests | leukemiasEset, RUnit, BiocGenerics |
| System Requirements | |
| URL | http://bioinfow.dep.usal.es/ |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | geNetClassifier_1.4.0.tar.gz |
| Windows Binary | geNetClassifier_1.4.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | geNetClassifier_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | geNetClassifier_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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