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Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene
Enrico Glaab1, Jaume Bacardit, Jonathan M Garibaldi
1Interdisciplinary Computing and Complex Systems Research Group, University of Nottingham, Nottingham, United Kingdom.
Plos One
|July 19, 2012
Summary
BioHEL and GAssist evolutionary systems create interpretable rule-based models for microarray cancer data analysis. These systems achieve high accuracy, outperforming traditional methods and aiding in identifying informative genes for disease research.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Microarray data analysis is crucial for cancer and genetic disease research.
- Classical machine learning methods face limitations with microarray data, including small sample sizes, high dimensionality, and noise.
- Enhanced interpretability of prediction models is needed to fully utilize microarray data.
Purpose of the Study:
- To evaluate rule-based evolutionary machine learning systems (BioHEL and GAssist) for microarray cancer data analysis.
- To develop simple, interpretable rule-based models for sample classification.
- To compare the performance of these systems against benchmark classifiers.
Main Methods:
- Application of BioHEL and GAssist on three public microarray cancer datasets.
- Development of simple if-then-else rule-based models for classification.
- Comparison with other microarray sample classifiers using diverse feature selection algorithms.
- Literature mining analysis of gene prioritization from classification rules.
Main Results:
- BioHEL and GAssist achieved accuracies above 90% in two-level external cross-validation.
- The evolutionary learning techniques demonstrated competitive performance against state-of-the-art methods like support vector machines.
- Gene prioritization from BioHEL's rules showed potential to outperform conventional ensemble feature selection methods.
Conclusions:
- Rule-based evolutionary machine learning systems offer a viable approach for accurate and interpretable microarray data analysis.
- These systems can effectively classify cancer samples and identify informative genes.
- The interpretability of the generated models facilitates a deeper understanding of the underlying biological data.
