Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analys...

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Bibliographic Details
Main Authors: Glaab, Enrico, Bacardit, Jaume, Garibaldi, Jonathan M., Krasnogor, Natalio
Format: Article
Published: Public Library of Science 2012
Subjects:
gene
protein
expression
microarray analysis
literature mining
classification
machine learning
prediction
cancer
cross-validation
sample classification
feature selection
Online Access:https://eprints.nottingham.ac.uk/1651/

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