Relational subgroup discovery for descriptive analysis of microarray data

Igor Trajkovski, Filip Železný, Jakub Tolar, Nada Lavrač

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to help find description of groups of genes differentially expressed in specific cancers. The descriptions are represented by means of relational features, extracted from gene ontology information, and are straightforwardly interpretable by the medical experts. We applied the proposed method to two known data sets: acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia and classification of fourteen types of cancer. Significant number of discovered groups of genes had a description, confirmed by the medical expert, which highlighted the underlying biological process that is responsible for distinguishing one class from the other classes. We view our methodology not just as a prototypical example of applying sophisticated machine learning algorithms to microarray data, but also as a motivation for developing more sophisticated functional annotations and ontologies, that can be processed by such learning algorithms.

Original languageEnglish (US)
Title of host publicationComputational Life Sciences II - Second International Symposium, CompLife 2006, Proceedings
PublisherSpringer- Verlag
Number of pages11
ISBN (Print)3540457674, 9783540457671
StatePublished - Jan 1 2006
EventSecond International Symposium on Computational Life Sciences, CompLife 2006 - Cambridge, United Kingdom
Duration: Sep 27 2006Sep 29 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4216 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherSecond International Symposium on Computational Life Sciences, CompLife 2006
Country/TerritoryUnited Kingdom


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