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

Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M.; Krasnogor, Natalio


Enrico Glaab

Jaume Bacardit

Jonathan M. Garibaldi

Natalio Krasnogor


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 analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.


Glaab, E., Bacardit, J., Garibaldi, J. M., & Krasnogor, N. (2012). Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PLoS ONE, 7(7), doi:10.1371/journal.pone.0039932

Journal Article Type Article
Publication Date Jul 1, 2012
Deposit Date Jul 17, 2012
Publicly Available Date Jul 17, 2012
Journal PLoS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 7
Issue 7
Article Number e39932
Keywords gene, protein, expression, microarray analysis, literature mining, classification, machine learning, prediction, cancer, cross-validation, sample classification, feature selection
Public URL
Publisher URL


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