Optimization of supervised self-organizing maps with genetic algorithms for classification electrophoretic profiles

Natalia Tomovska, Igor Kuzmanovski, Kiro Stojanoski

Abstract


Standard electrophoresis methods were used in the classification of analyzed proteins in cerebrospinal fluid from patients with multiple sclerosis. Disc electrophoresis was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid on polyacrylamide gel, mainly with multiple sclerosis and other central nervous system dysfunctions. ImageMaster 1D Elite and GelPro specialized software packages were used for fast accurate image and gel analysis. The classification model was based on supervised self-organizing maps. In order to perform the modeling in automated manner genetic algorithms were used. Using this approach and a data set composed of 69 samples we were able to develop models based on supervised self-organizing maps which were able to correctly classify 83 % of the samples in the data set used for external validation.


Keywords


disc electrophoresis, cerebrospinal fluid, protein analysis, supervised self-organizing maps

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References


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DOI: http://dx.doi.org/10.20450/mjcce.2014.436

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