Abstract
In this work we investigate the application of predictive clustering trees (PCTs) for analysing gene expression data. PCTs provide a flexible approach for both predictive and descriptive analysis, both often used on gene expression data. To begin with, we use gene expression data for building predictive models for associated clinical data, where we compare single-target with multi-target models. Related to this, random forests of PCTs (single and multi-target) are used to assess the importance of individual genes w.r.t. the clinical parameters. For a more descriptive analysis, we perform a so-called constrained clustering of expression data. Also, we extend the descriptive analysis to take into account a temporal component, by using PCTs for finding descriptions of short time series of gene expression data.
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Slavkov, I., Džeroski, S. (2010). Analyzing Gene Expression Data with Predictive Clustering Trees. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_16
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DOI: https://doi.org/10.1007/978-1-4419-7738-0_16
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