Background. Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information. Methods. We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model. Results. A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules. Conclusions. The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.

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基于遗传算法的支持向量机在银屑病分类基因表达特征识别中的应用：混合模型

背景。牛皮癣是一种慢性自身免疫性疾病，严重损害患者的生活质量。该疾病的诊断是由皮肤科医生通过目视检查病变皮肤来完成的。利用基因表达对银屑病进行分类是早期有效治疗该疾病的一个重要问题。因此，基因表达数据和选择合适的基因特征是有效的信息来源。方法。我们的目标是开发一种基于遗传算法和支持向量机（SVM）两种机器学习模型的牛皮癣诊断混合分类器。该方法还进行基因签名选择。使用公开的基因表达数据集来测试该模型。结果。使用混合模型从原始 54,675 个探针中选择了 181 个探针集，预测精度超过测试集 100%。使用蛋白质-蛋白质相互作用网络鉴定了 10 个中心基因。十分之九的已识别基因在重要模块中被发现。结论。结果表明，遗传算法显着提高了 SVM 分类器的性能，这意味着该模型能够检测相关基因表达特征作为最佳特征。