Gene expression mining guided by background knowledge

Jiří Kléma, Filip Železný, Igor Trajkovski, Filip Karel, Bruno Crémilleux, Jakub Tolar

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationData Mining and Medical Knowledge Management
Subtitle of host publicationCases and Applications
PublisherIGI Global
Pages268-292
Number of pages25
ISBN (Print)9781605662183
DOIs
StatePublished - 2009

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  • Cite this

    Kléma, J., Železný, F., Trajkovski, I., Karel, F., Crémilleux, B., & Tolar, J. (2009). Gene expression mining guided by background knowledge. In Data Mining and Medical Knowledge Management: Cases and Applications (pp. 268-292). IGI Global. https://doi.org/10.4018/978-1-60566-218-3.ch013