Microarchitecture of a multicore SoC for data analysis of a lab-on-chip microarray
EURASIP Journal on Advances in Signal Processing
A soft multi-core architecture for edge detection and data analysis of microarray images
Journal of Systems Architecture: the EUROMICRO Journal
Microprocessors & Microsystems
Learning to discover faulty spots in cDNA microarrays
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Classification and retrieval on macroinvertebrate image databases
Computers in Biology and Medicine
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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.