Abstract
To emphasize gene interactions in the classification algorithms, a new representation is proposed, comprising gene-pairs and not single genes. Each pair is represented by L 1 difference in the corresponding expression values. The novel representation is evaluated on benchmark datasets and is shown to often increase classification accuracy for genetic datasets. Exploiting the gene-pair representation and the Gene Ontology (GO), the semantic similarity of gene pairs can be incorporated to pre-select pairs with a high similarity value. The GO-based feature selection approach is compared to the plain data driven selection and is shown to often increase classification accuracy.
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Schön, T., Tsymbal, A., Huber, M. (2010). Gene-Pair Representation and Incorporation of GO-based Semantic Similarity into Classification of Gene Expression Data. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_24
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DOI: https://doi.org/10.1007/978-3-642-13529-3_24
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