exprso (0.1.7)

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.

Maintainer: Thomas Quinn
Author(s): Thomas Quinn [aut, cre], Daniel Tylee [ctb]

License: GPL-2

Uses: cluster, e1071, kernlab, lattice, MASS, mRMRe, nnet, pathClass, plyr, randomForest, ROCR, sampling, testthat, knitr, magrittr, h2o, rmarkdown

Released about 3 years ago.