ArticleBioMed research international2021
Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Advancements in psoriasis classification using custom transfer learning algorithms.Scientific reports · 2026Article
- In Silico Transcriptomic Analysis for Identification of Potential Diagnostic and Prognostic Biomarkers and Therapeutic Targets in Cervical Cancer using a Hybrid Genetic Algorithm-Support Vector Machine Approach.Archives of Iranian medicine · 2025Article
- Article
- Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.Scientific reports · 2023Article
- Psoriasis Associated Hub Genes Revealed by Weighted Gene Co-Expression Network Analysis.Cell journal · 2023Article
- Diagnostic Test Accuracy of Genetic Tests in Diagnosing Psoriasis: A Systematic Review.Cureus · 2022Review
- Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.BioMed research international · 2022Article
- Emerging Uses of Artificial Intelligence in Chronic Dermatologic Disease: A Scoping Review.Journal of cutaneous medicine and surgeryArticle
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Authors and funding
5 authors.
Funding
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Abstract
backgroundPsoriasis 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.
methodsWe 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.
resultsA 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.
conclusionsThe 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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