Abstract
This paper presents a novel approach to building sample classifiers based on microarray gene expression studies. This approach differs from standard methods in the way features are selected. Standard methods focus on features (genes) with most differential expression between classes of samples compared, while the proposed approach takes into account apriori domain knowledge of relationships between features, available e.g., in the form of pathway or gene-ontology databases. Features for classification are then selected on the basis of activation of pathways (gene sets) rather than mutually unrelated genes with very high individual predictive power. Performance of the proposed method is illustrated on the basis of sample microarray studies.
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Maciejewski, H. (2011). Class Prediction in Microarray Studies Based on Activation of Pathways. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_41
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DOI: https://doi.org/10.1007/978-3-642-21219-2_41
Publisher Name: Springer, Berlin, Heidelberg
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