Reference : Using rule-based machine learning for candidate disease gene prioritization and sampl...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data
Glaab, Enrico mailto [University of Nottingham]
Bacardit, Jaume [University of Nottingham]
Garibaldi, Jonathan M. [University of Nottingham]
Krasnogor, Natalio [University of Nottingham]
Public Library of Science
39932 - 39932
Yes (verified by ORBilu)
[en] microarray ; cancer ; machine learning ; gene expression ; disease ; prediction ; classification ; feature selection ; rule learning ; evolutionary learning
[en] 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.

File(s) associated to this reference

Fulltext file(s):

Open access
journal.pone.0039932.pdfNo commentaryPublisher postprint553.43 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.