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

Authors

  • Natalia Tomovska Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril & Methodius University, Skopje
  • Igor Kuzmanovski Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril & Methodius University, Skopje
  • Kiro Stojanoski Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril & Methodius University, Skopje

DOI:

https://doi.org/10.20450/mjcce.2014.436

Keywords:

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

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.

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Published

2014-05-02

How to Cite

Tomovska, N., Kuzmanovski, I., & Stojanoski, K. (2014). Optimization of supervised self-organizing maps with genetic algorithms for classification electrophoretic profiles. Macedonian Journal of Chemistry and Chemical Engineering, 33(1), 65–71. https://doi.org/10.20450/mjcce.2014.436

Issue

Vol. 33 No. 1 (2014)

Section

Analytical Chemistry

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