INTELLIGENT ANALYSIS OF GENOMIC MEASUREMENTS

I.N. Flaounas, D.K. Iakovidis, D.E. Maroulis, S.A. Karkanis

Abstract

In this paper we propose a methodology for intelligent analysis of genomic measurements. It is based on a sequential scheme of Support Vector Machines and it can be used for class prediction of multiclass genomic samples. The proposed methodology was evaluated using two lung cancer datasets. The results are comparable and in many cases higher to the accuracy of relevant methodologies that have been proposed in the literature.

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IMEKO TC

TC4 - Measurement of Electrical Quantities

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