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

Abstract

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