OPTIMIZATION OF SUPERVISED SELF-ORGANIZING MAPS WITH GENETIC ALGORITHMS FOR CLASSIFICATION ELECTROPHORETIC PROFILES

被引：1

作者：

Tomovska, Natalija ^{[1
]}

Kuzmanovski, Igor ^{[1
]}

Stojanoski, Kiro ^{[1
]}

机构：

[1] Ss Cyril & Methodius Univ, Fac Nat Sci & Math, Inst Chem, Skopje, Macedonia

来源：

MACEDONIAN JOURNAL OF CHEMISTRY AND CHEMICAL ENGINEERING | 2014年 / 33卷 / 01期

关键词：

disc electrophoresis; cerebrospinal fluid; protein analysis; supervised self-organizing maps; ARTIFICIAL NEURAL-NETWORKS; WAVELENGTH SELECTION; MULTIPLE-SCLEROSIS; KOHONEN; SPECTROMETRY; PARTICLES;

D O I：

10.20450/mjcce.2014.436

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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 packages 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 supervised self-organizing maps which were able to correctly classify 83% of the samples in the data set used for external validation.

引用

页码：65 / 71

页数：7

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