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.

Maintainer: Thomas Quinn <contacttomquinn at gmail.com>

Author(s): Thomas Quinn*, Daniel Tylee*

Install package and any missing dependencies by running this line in your R console:

install.packages("exprso")

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
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Package exprso
Materials
URL http://github.com/tpq/exprso
Task Views
Version 0.1.8
Published 2016-12-23
License GPL-2
BugReports http://github.com/tpq/exprso/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks exprso check results
Package source exprso_0.1.8.tar.gz