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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Public Library of Science
2012
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| Online Access: | https://eprints.nottingham.ac.uk/1651/ |
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| author | Glaab, Enrico Bacardit, Jaume Garibaldi, Jonathan M. Krasnogor, Natalio |
| author_facet | Glaab, Enrico Bacardit, Jaume Garibaldi, Jonathan M. Krasnogor, Natalio |
| author_sort | Glaab, Enrico |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:15:56Z |
| format | Article |
| id | nottingham-1651 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:15:56Z |
| publishDate | 2012 |
| publisher | Public Library of Science |
| recordtype | eprints |
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| spelling | nottingham-16512020-05-04T20:21:32Z https://eprints.nottingham.ac.uk/1651/ 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 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. Public Library of Science 2012-07 Article PeerReviewed Glaab, Enrico, Bacardit, Jaume, Garibaldi, Jonathan M. and Krasnogor, Natalio (2012) Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PLoS ONE, 7 (7). e39932. ISSN 1932-6203 gene protein expression microarray analysis literature mining classification machine learning prediction cancer cross-validation sample classification feature selection http://dx.doi.org/10.1371/journal.pone.0039932 doi:10.1371/journal.pone.0039932 doi:10.1371/journal.pone.0039932 |
| spellingShingle | gene protein expression microarray analysis literature mining classification machine learning prediction cancer cross-validation sample classification feature selection Glaab, Enrico Bacardit, Jaume Garibaldi, Jonathan M. Krasnogor, Natalio Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title | Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title_full | Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title_fullStr | Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title_full_unstemmed | Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title_short | Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| title_sort | using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data |
| topic | gene protein expression microarray analysis literature mining classification machine learning prediction cancer cross-validation sample classification feature selection |
| url | https://eprints.nottingham.ac.uk/1651/ https://eprints.nottingham.ac.uk/1651/ https://eprints.nottingham.ac.uk/1651/ |