Grid-Based knowledge discovery in clinico-genomic data

Authors:
Michael May;George Potamias;Stefan Rüping
Affiliations:
Fraunhofer AIS, Schloss Birlinghoven, St. Augustin, Germany;Institute of Computer Science, FORTH, Heraklion, Crete, Greece;Fraunhofer AIS, Schloss Birlinghoven, St. Augustin, Germany
Venue:
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Year:
2006

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Abstract

Knowledge discovery in clinico-genomic data is a task that requires to integrate not only highly heterogeneous kinds of data, but also the requirements and interests of very different user groups. Technologies of grid computing promise to be an effective tool to combine all these requirements into a single architecture. In this paper, we describe scenarios and future research directions related to grid-based knowledge discovery in clinico-genomic data, and introduce the approach taken by the recently launched ACGT project. The whole endeavor is considered in the context of biomedical informatics research and aims towards the realization of an integrated and grid-enabled biomedical infrastructure. The presented integrated clinico-genomics knowledge discovery (ICGKD) scenario and its process realization is based on a multi-strategy data-mining approach that seamlessly integrates three distinct data-mining components: clustering, association rules mining, and feature-selection. Preliminary experimental results are indicative of the rational and reliability of the approach.