Abstract
In this paper, the problem of classifying the quality of microarray data spots is addressed, using concepts derived from the supervised learning theory. The proposed method, after extracting spots from the microarray image, computes several features, which take into account shape, color and variability. The features are classified using support vector machines, a recent statistical classification technique that is being employed widely. The proposed method does not make any assumptions on the problem and does not require any a priori information. The proposed system has been tested in a real case, for several different parameters’ configurations. Experimental results show the effectiveness of the proposed approach, also in comparison with state-of-the-art methods.
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Notes
The B-Course method used by the authors to infer the structure of the Naive Bayesian Networks merely represents a feature selection step.
The bleeding could be defined as the phenomenon in which a spot spreads so much that it is mixed with its neighbors should be carefully avoided.
In classification, only the sign is used, not the magnitude.
Data, together with experiments’ descriptions, data specifications, figures, experts’ classifications and labels are available on the web site http://sigwww.cs.tut.fi/TICSP/SpotQuality/.
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Acknowledgements
The authors would like to thank very much Dr. Sampsa Hautaniemi of Tampere University of Technology (Finland), for kindly supplied the microarray data and the features used for testing. The authors would like to thank also Dr. S. Barbi and Prof. A. Scarpa of the Department of Pathology of the University of Verona (Italy) for helpful discussions. Finally, the authors would like to thank G.E. Felis for carefully reading the paper.
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Bicego, M., Del Rosario Martinez, M. & Murino, V. A supervised data-driven approach for microarray spot quality classification. Pattern Anal Applic 8, 181–187 (2005). https://doi.org/10.1007/s10044-005-0254-5
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DOI: https://doi.org/10.1007/s10044-005-0254-5