Supporting clinico-genomic knowledge discovery: a multi-strategy data mining process

Authors:
Alexandros Kanterakis;George Potamias
Affiliations:
Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece;Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

We present a combined clinico-genomic knowledge discovery (CGKD) process suited for linking gene-expression (microarray) and clinical patient data. The process present a multi-strategy mining approach realized by the smooth integration of three distinct data-mining components: clustering (based on a discretized k-means approach), association rules mining, and feature-selection for selecting discrimant genes. The proposed CGKD process is applied on a real-world gene-expression profiling study (i.e., clinical outcome of breast cancer patients). Assessment of the results demonstrates the rationality and reliability of the approach.