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. 2021 Sep 8:2021:5520710.
doi: 10.1155/2021/5520710. eCollection 2021.

Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model

Leili Tapak  1   2 , Saeid Afshar  3   4 , Mahlagha Afrasiabi  5 , Mohammad Kazem Ghasemi  1 , Pedram Alirezaei  6
Affiliations

Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model

Leili Tapak et al. Biomed Res Int. .

Abstract

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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Conflict of interest statement

The authors declare that there is no conflict of interests.

Figures

Figure 1
Figure 1
Linear separation of two classes with a support vector machine classifier. Samples on the margin are called the support vectors.
Figure 2
Figure 2
Overview of the hybrid GA-SVM model.
Figure 3
Figure 3
The ROC curves of the four scenarios of classification of psoriasis patients using (a) SVM with 54,657 features, (b) GA+SVM with 27,265 features, (c) SVM with 445 features, and (d) GA+SVM with 181 features.
Figure 4
Figure 4
Protein-protein interaction network. The PPI network was constructed using STRING and visualized with Cytoscape. The selected top 10 genes with a high degree were shown in green. The nodes related to the significant module were shown in ellipse shapes.
Figure 5
Figure 5
Gene Ontology and KEGG pathway enrichment analysis. The KEGG pathway and GO enrichment analysis for selected probes with GA+SVM were performed using the DAVID database.
Figure 6
Figure 6
Gene Ontology and KEGG pathway enrichment analysis.
Figure 7
Figure 7
Molecular g = function and biological process.
See this image and copyright information in PMC

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