Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The nature of statistical learning theory
The nature of statistical learning theory
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Principles of Data Mining
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The results of a gene expression study are difficult to interpret. To increase interpretability, researchers have developed classification techniques that produce rules to classify gene expression profiles. Genetic programming is one method to produce classification rules. These rules are difficult to interpret because they are based on complicated functions of gene expression values. We propose the binary rule majority voting genetic programming classifier BRMVGPC that classifies samples using binary rules based on the detection calls for genes instead of the gene expression values. BRMVGPC increases rule interpretability. We evaluate BRMVGPC on two public datasets, one brain and one prostate cancer, and achieved 88.89% and 86.39% accuracy respectively. These results are comparable to other classifiers in the gene expression profile domain. Specific contributions include a classification technique BRMVGPC and an iterative k-nearest neighbour technique for handling marginal detection call values.