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Gene Expression Mining Guided by Background Knowledge
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Author(s): Jirí Kléma (Czech Technical University in Prague, Czech Republic), Filip Železný (Czech Technical University in Prague, Czech Republic), Igor Trajkovski (Jožef Stefan Institute, Slovenia), Filip Karel (Czech Technical University in Prague, Czech Republic)and Bruno Crémilleux (Université de Caen, France)
Copyright: 2009
Pages: 25
Source title:
Data Mining and Medical Knowledge Management: Cases and Applications
Source Author(s)/Editor(s): Petr Berka (University of Economics, Prague, Czech Republic), Jan Rauch (University of Economics, Prague, Czech Republic)and Djamel Abdelkader Zighed (University of Lumiere Lyon 2, France)
DOI: 10.4018/978-1-60566-218-3.ch013
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Abstract
This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several tasks such as relational descriptive analysis, constraintbased knowledge discovery, feature selection and construction or quantitative association rule mining. The chapter also accentuates diversity of background knowledge. In genomics, it can be stored in formats such as free texts, ontologies, pathways, links among biological entities, and many others. The authors hope that understanding of automated integration of heterogeneous data sources helps researchers to reach compact and transparent as well as biologically valid and plausible results of their gene-expression data analysis.
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