MLSeq-package: Machine learning interface for RNA-Seq data

In dncR/MLSeq: Machine Learning Interface for RNA-Seq Data

Description

This package applies machine learning methods, such as Support Vector Machines (SVM), Random Forest (RF), Classification and Regression Trees (CART), Linear Discriminant Analysis (LDA) and more to RNA-Seq data. MLSeq combines well-known differential expression algorithms from bioconductor packages with functions from a famous package caret, which has comprehensive machine learning algorithms for classification and regression tasks. Although caret has 200+ classification/regression algorithm built-in, approximately 85 classification algorithms are used in MLSeq for classifying gene-expression data. See availableMethods() for further information.

Author(s)

Dincer Goksuluk, Gokmen Zararsiz, Selcuk Korkmaz, Vahap Eldem, Ahmet Ozturk and Ahmet Ergun Karaagaoglu

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Maintainers:

Dincer Goksuluk [e-mail]

Gokmen Zararsiz, [e-mail]

Selcuk Korkmaz [e-mail]

See Also

availableMethods, getModelInfo

Package:	MLSeq
Type:	Package
License:	GPL (>= 2)

dncR/MLSeq documentation built on May 17, 2020, 6:45 p.m.