title: Semantic subgroup discovery: using ontologies in microarray data analysis.
abstract: A major challenge for next generation data mining systems is creative knowledge discovery from highly diverse and distributed data and knowledge sources. This paper presents an approach to information fusion and creative knowledge discovery from semantically annotated knowledge sources: by using ontology information as background knowledge for semantic subgroup discovery, rules are constructed that allow the expert to recognize gene groups that are differentially expressed in different types of tissues. The paper presents also current directions in creative knowledge discovery through bisociative data analysis, illustrated on a systems biology case study.
authors: Jozef Stefan Institute, Jamova 39, Ljubljana, Slovenia. nada.lavrac@ijs.si
authors: Nada
authors: N
authors: Lavrac
authors: Petra Kralj
authors: PK
authors: Novak
authors: Igor
authors: I
authors: Mozetic
authors: Vid
authors: V
authors: Podpecan
authors: Helena
authors: H
authors: Motaln
authors: Marko
authors: M
authors: Petek
authors: Kristina
authors: K
authors: Gruden
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D046228:Microarray Analysis / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics