Browsing by Author "Qaqish, Bahjat F."

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The Effect of Different Classifiers on Recursive Cluster Elimination in the Analysis of Transcriptomic Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Bulut, Nurten; Bakir-Gungor, Burcu; Qaqish, Bahjat F.; Yousef, Malik; 0000-0002-1895-8749; 0000-0002-2272-6270; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Bulut, Nurten; Bakir-Gungor, Burcu
Gene expression data with limited sample size and a large number of genes are frequently encountered in genetic studies. In such high-dimensional data, identification of genes that distinguish between disease states is a challenging task. Feature selection (FS) is a useful approach in dealing with high dimensionality. Support Vector Machines Recursive Cluster Elimination (SVM-RCE) is a technique for FS in highdimensional data. The SVM-RCE approach has been utilized for identification of clusters of genes whose expression levels correlate with pathological state. A key step in SVM-RCE is the use of an SVM classifier to assign an area under the curve (AUC) score to each gene cluster based on its ability to predict class labels. In this study, we investigate the use of alternative classifiers in the cluster-scoring step. Specifically, we compare Support Vector Machines, Random Forest, XgBoost, Naive Bayes, and linear logistic regression. In addition to AUC score performance evaluation, the algorithms are compared in terms of the number of selected genes at different levels of clustering and in terms of the running time.
Effect of Recursive Cluster Elimination with Different Clustering Algorithms Applied to Gene Expression Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Qaqish, Bahjat F.; Yousef, Malik.; 0000-0002-2272-6270; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Bakir-Gungor, Burcu
Feature selection (FS) is an effective tool in dealing with high dimensionality and reducing computational cost. Support Vector Machines – Recursive Cluster Elimination (SVM-RCE) is one of several algorithms that have been developed for FS in high dimensional data. SVM-RCE involves a clustering step which originally is k-means. Using various performance metrics, three alternative algorithms are evaluated in this context; k-medoids, Hierarchical Clustering (HC), and Gaussian Mixture Model (GMM). Comparisons will be carried out on five publicly available gene expression datasets. The results show that k-means in SVM-RCE obtains higher performance than other tested algorithms in terms of classification performance. Additionally, HC shows a similar performance to k-means. Our findings show superiority of using k-means. This study can contribute to the development of SVMRCE with different variations, leading to decrease in the number of selected genes, and an increase in prediction performance.