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

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.

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

“…G β 5 is part of a signal-transducing G-protein β subunit family. G β 5 is preferentially expressed in the brain and the nervous system, and it is unique in its ability to heterodimerize with G-protein signal-regulating protein family R7 [ 15 , 16 ]. After binding to the R7 proteins, G β 5 forms a complex with SNARE-like membrane-anchoring proteins [ 17 ].…”

“…The support vector machine (SVM) is a regression/classification method that is based on learning [ 14 , 15 ]. Let us consider y as the binary response taking its labels as -1 and +1 (here, PD patients and healthy individuals) and X as the vector of p genes (gene expression profiling).…”

Psoriasis Identification from Gene Expression Analysis Using Relative Entropy Fuzzy Score Normalization, Genetic Weighted K-Nearest Neighbors Imputation and Hybrid Machine Learning Classifier

“…The proposed MKUSVM-RBFELM classifier method is evaluated and compared with existing NB [21], ANN [22], SVM [23] and ELM [24]. The comparisons are made in terms of accuracy (A), precision (P), recall (R), f-measure (F) and mathew correlation coefficient (MCC).…”

“…The gene expression profiles related to the psoriasis treatment were utilized from the publically available psoriasis whole blood transcriptome dataset named GSE55201 created using the affymetrix human genome U133 plus (microarray) with platform ID GPL570 [25]. This open-source dataset is available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?ac c=GSE55201 [23]). It contains samples collected from 81 patients who have volunteered for interleukin IL-7 treatments.…”

Automatic Image Characterization of Psoriasis Lesions

“…For example, Shrivastava et al [12] proposed a study focusing a psoriasis risk assessment (pRAS) based on two kinds of classifiers (i.e., support vector machines and decision trees). Other ML-related works related to psoriasis studies are those proposed by [13][14][15]. Regarding DL-related studies, some recent works have been developed by other authors in the field of dermatology [16] and, specifically, related to the diagnosis of psoriasis [17].…”