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.
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May, M., Potamias, G., Rüping, S. (2006). Grid-Based Knowledge Discovery in Clinico-Genomic Data. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_20
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DOI: https://doi.org/10.1007/11946465_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68063-5
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