exprso: Rapid Implementation of Machine Learning Algorithms for Genomic Data

Supervised machine learning has an increasingly important role in biological studies. However, the sheer complexity of classification pipelines poses a significant barrier to the expert biologist unfamiliar with machine learning. Moreover, many biologists lack the time or technical skills necessary to establish their own pipelines. This package introduces a framework for the rapid implementation of high-throughput supervised machine learning built with the biologist user in mind. Written by biologists, for biologists, this package provides a user-friendly interface that empowers investigators to execute state-of-the-art binary and multi-class classification, including deep learning, with minimal programming experience necessary.

Version: 0.1.8
Depends: R (≥ 3.2.2), kernlab
Imports: affy, Biobase, cluster, MASS, e1071, lattice, methods, mRMRe, nnet, pathClass, plyr, stats, randomForest, ROCR, sampling
Suggests: GEOquery, h2o, golubEsets, knitr, limma, magrittr, rmarkdown, testthat
Published: 2016-12-23
Author: Thomas Quinn [aut, cre], Daniel Tylee [ctb]
Maintainer: Thomas Quinn <contacttomquinn at gmail.com>
BugReports: http://github.com/tpq/exprso/issues
License: GPL-2
URL: http://github.com/tpq/exprso
NeedsCompilation: no
Citation: exprso citation info
Materials: README NEWS
CRAN checks: exprso results


Reference manual: exprso.pdf
Vignettes: Advanced Topics for the exprso Package
The exprso Cheatsheet
An Introduction to the exprso Package
Use Disclaimer, Please Read
Package source: exprso_0.1.8.tar.gz
Windows binaries: r-devel: exprso_0.1.8.zip, r-release: exprso_0.1.8.zip, r-oldrel: exprso_0.1.8.zip
OS X Mavericks binaries: r-release: exprso_0.1.8.tgz, r-oldrel: exprso_0.1.8.tgz
Old sources: exprso archive


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