Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Biomed Res Int. 2021 Sep 8;2021:5520710. doi: 10.1155/2021/5520710. eCollection 2021.
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.
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.
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.
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.
银屑病是一种慢性自身免疫性疾病，显著影响患者的生活质量。该疾病的诊断是由皮肤科医生通过对皮损皮肤的目视检查来完成的。使用基因表达对银屑病进行分类是实现疾病早期和有效治疗的重要问题。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。
我们旨在基于遗传算法和支持向量机（SVM）两种机器学习模型开发一种用于银屑病诊断的混合分类器。该方法还进行了基因特征选择。使用公开的基因表达数据集来测试模型。
使用混合模型从最初的 54675 个探针中选择了 181 个探针，在测试集上的预测准确率达到 100%。使用蛋白质-蛋白质相互作用网络鉴定了 10 个枢纽基因。鉴定出的 10 个基因中有 9 个存在于显著模块中。
结果表明，遗传算法显著提高了 SVM 分类器的性能，这意味着所提出的模型在检测相关基因表达特征方面具有优势，这些特征是最佳特征。
