Semantic subgroup discovery and cross-context linking for microarray data analysis

Igor Mozetič, Nada Lavrač, Vid Podpečan, Petra Kralj Novak, Helena Motaln, Marko Petek, Kristina Gruden, Hannu Toivonen, Kimmo Kulovesi

Research output: Contribution to Book/Report typesChapterpeer-review

Abstract (may include machine translation)

The article presents an approach to computational knowledge discovery through the mechanism of bisociation. Bisociative reasoning is at the heart of creative, accidental discovery (e.g., serendipity), and is focused on finding unexpected links by crossing contexts. Contextualization and linking between highly diverse and distributed data and knowledge sources is therefore crucial for the implementation of bisociative reasoning. In the article we explore these ideas on the problem of analysis of microarray data. We show how enriched gene sets are found by using ontology information as background knowledge in semantic subgroup discovery. These genes are then contextualized by the computation of probabilistic links to diverse bioinformatics resources. Preliminary experiments with microarray data illustrate the approach.

Original languageEnglish
Title of host publicationBisociative Knowledge Discovery
Subtitle of host publicationAn Introduction to Concept, Algorithms, Tools, and Applications
EditorsMichael R. Berthold
Pages379-389
Number of pages11
DOIs
StatePublished - 2012
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7250
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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