Gene-pair representation and incorporation of GO-based semantic similarity into classification of gene expression data

Authors:
Torsten Schön;Alexey Tsymbal;Martin Huber
Affiliations:
Hochschule Weihenstephan-Triesdorf, Freising, Germany and Corporate Technology Div., Siemens AG, Erlangen, Germany;Corporate Technology Div., Siemens AG, Erlangen, Germany;Corporate Technology Div., Siemens AG, Erlangen, Germany
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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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 L1 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.