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A data mining based approach for collaborative analysis of biomedical data

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

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International journal on artificial intelligence tools 23 (2014), Nr.4, Art. 1460010, 21 S.
ISSN: 0218-2130 (Print)
ISSN: 1793-6349 (Online)
Englisch
Zeitschriftenaufsatz
Fraunhofer IAIS ()

Abstract
Biomedical research becomes increasingly multidisciplinary and collaborative in nature. At the same time, it has recently seen a vast growth in publicly and instantly available information. 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 genomic data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and interpreting biomedical 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. Evaluation results show that the proposed approach facilitates users to set their research objectives and better understand the data and methodologies used in their research.

: http://publica.fraunhofer.de/dokumente/N-351801.html