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Data mining based collaborative analysis of microarray data

 
: Tsiliki, G.; Kossida, S.; Friesen, N.; Rueping, S.; Tzagarakis, M.; Karacapilidis, N.

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IEEE Computer Society:
IEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012. Vol.1, Part 2 : Athens, Greece, 7 - 9 November 2012. Proceedings; including the workshop: Fifth IEEE International Symposium on Monitoring & Surveillance Research (ISMSR)]
Piscataway, NJ: IEEE, 2012
ISBN: 978-1-479-90227-9
ISBN: 978-0-7695-4915-6
S.682-689
International Conference on Tools with Artificial Intelligence (ICTAI) <24, 2012, Athens>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
Biomedical research has recently seen a vast growth in publicly and instantly available information, which are often complementary or overlapping. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data-mining algorithm to gene-expression data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and biologically interpreting gene-expression micro array data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data.

: http://publica.fraunhofer.de/dokumente/N-254212.html