Semantic subgroup discovery: using ontologies in microarray data analysis.
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.
Lavrac
Nada
N
Jozef Stefan Institute, Jamova 39, Ljubljana, Slovenia. nada.lavrac@ijs.si
Novak
Petra Kralj
PK
Mozetic
Igor
I
Podpecan
Vid
V
Motaln
Helena
H
Petek
Marko
M
Gruden
Kristina
K
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