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.
|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|
|Author:||Thomas Quinn [aut, cre], Daniel Tylee [ctb]|
|Maintainer:||Thomas Quinn <contacttomquinn at gmail.com>|
|Citation:||exprso citation info|
|CRAN checks:||exprso results|
Advanced Topics for the exprso Package
The exprso Cheatsheet
An Introduction to the exprso Package
Use Disclaimer, Please Read
|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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