Rapid Implementation of Machine Learning Algorithms for Genomic
Data
c(
person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
person("Daniel", "Tylee", email = "dantylee@gmail.com", role = "ctb")
)
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.
cluster, MASS, e1071, lattice, methods, mRMRe, nnet,
pathClass, plyr, stats, randomForest, ROCR, sampling
Biobase, GEOquery, h2o, golubEsets, knitr, limma, magrittr,
rmarkdown, testthat
Thomas Quinn [aut, cre],
Daniel Tylee [ctb]

affy, Biobase, cluster, MASS, e1071, lattice, methods, mRMRe,
nnet, pathClass, plyr, stats, randomForest, ROCR, sampling
GEOquery, h2o, golubEsets, knitr, limma, magrittr, rmarkdown,
testthat

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly implementing supervised
machine learning in a high-throughput manner. 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.
cluster, MASS, e1071, lattice, magrittr, methods, nnet, plyr,
stats, randomForest, ROCR, sampling
Biobase, edgeR, GEOquery, h2o, knitr, limma, mRMRe,
pathClass, propr, rmarkdown, testthat

Rapid Deployment of Machine Learning Algorithms
Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
programming experience necessary.
cluster, MASS, e1071, lattice, methods, nnet, plyr, stats,
randomForest, ROCR, sampling
Biobase, edgeR, GEOquery, h2o, knitr, limma, magrittr, mRMRe,
pathClass, propr, rmarkdown, testthat