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| Title: | LEARNING COMPREHENSIBLE CLASSIFICATION RULES FROM GENE EXPRESSION DATA USING GENETIC PROGRAMMING AND BIOLOGICAL ONTOLOGIES | |
| DOI No: | 10.1142/9789812774118_0081 | |
| Source: | APPLIED ARTIFICIAL INTELLIGENCE (pp 573-578) | |
| Author(s): | BEN GOERTZEL
Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA LÚCIO DE SOUZA COELHO Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA CASSIO PENNACHIN Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA IZABELA FREIRE GOERTZEL Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA MURILO SARAIVA DE QUEIROZ Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA FRANCISCO PROSDOCIMI Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA FRANCISCO PEREIRA LOBO Biomind LLC, 1405 Bernerd Place, Rockville, MD 20851, USA |
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| Abstract: | We consider the problem of how to use automated techniques to learn simple and compact classification rules from microarray gene expression data. Our approach employs the traditional “genetic programming” (GP) algorithm as a supervised categorization technique, but rather than applying GP to gene expression vectors directly, it applies GP to “enhanced feature vectors” obtained by preprocessing the gene expression data using the Gene Ontology and PIR ontologies. On the two datasets considered, this “GP + enhanced feature vectors” combination succeeds in producing compact and simple classification models with near-optimal classification accuracy. For sake of comparison, we also give results from the combination of support vector machine classification and enhanced feature vectors on the same datasets. | |
| Full Text: | View full text in PDF format (293KB) | |
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