Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Start date: 2020Subject: search.filters.subject.Feature Selection

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Now showing 1 - 10 of 29
  • Article
    Citation - Scopus: 4
    RCE-IFE: Recursive Cluster Elimination with Intra-Cluster Feature Elimination
    (PeerJ Inc., 2025-02-07) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Qaqish, Bahjat; Yousef, Malik
  • Conference Object
    Impact of Gene Duplicate Handling Strategies on Classification Performance and Feature Selection in Gene Expression Data
    (Institute of Electrical and Electronics Engineers Inc., 2025-09-17) Kuzudisli, Cihan; Qaqish, Bahjat; Gungor, Burcu Bakir; Yousef, Malik
  • Conference Object
    Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach
    (IEEE, 2025-06-25) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    Integrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.
  • Correction
    Correction: Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records
    (Frontiers Media S.A., 2025-08-29) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
  • Article
    Citation - WoS: 26
    Citation - Scopus: 33
    miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules
    (Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, Burcu
    Increasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.
  • Conference Object
    The Effect of Different Classifiers on Recursive Cluster Elimination in the Analysis of Transcriptomic Data
    (Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Bulut, Nurten; Bakir-Güngör, Burcu; Qaqish, Bahjat F.; Yousef, Malik
    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 high-dimensional 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. © 2023 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    The Determination of Distinctive Single Nucleotide Polymorphism Sets for the Diagnosis of Behcet's Disease
    (IEEE Computer Soc, 2022-05-01) Isik, Yunus Emre; Gormez, Yasin; Aydin, Zafer; Bakir-Gungor, Burcu
    Behcet's Disease (BD) is a multi-system inflammatory disorder in which the etiology remains unclear. The most probable hypothesis is that genetic tendency and environmental factors play roles in the development of BD. In order to find the essential reasons, genetic changes on thousands of genes should be analyzed. Besides, there is a need for extra analysis to find out which genetic factor affects the disease. Machine learning approaches have high potential for extracting the knowledge from genomics and selecting the representative Single Nucleotide Polymorphisms (SNPs) as the most effective features for the clinical diagnosis process. In this study, we have attempted to identify representative SNPs using feature selection methods, incorporating biological information and aimed to develop a machine-learning model for diagnosing Behcet's disease. By combining biological information and machine learning classifiers, up to 99.64 percent accuracy of disease prediction is achieved using only 13,611 out of 311,459 SNPs. In addition, we revealed the SNPs that are most distinctive by performing repeated feature selection in cross-validation experiments.
  • Article
    Citation - WoS: 10
    Citation - Scopus: 15
    Textnettopics Pro, a Topic Model-Based Text Classification for Short Text by Integration of Semantic and Document-Topic Distribution Information
    (Frontiers Media S.A., 2023-10-05) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
    With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.
  • Conference Object
    TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling
    (Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
    TextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.
  • Article
    Citation - WoS: 48
    Citation - Scopus: 65
    Review of Feature Selection Approaches Based on Grouping of Features
    (PeerJ Inc, 2023-07-17) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Bulut, Nurten; Qaqish, Bahjat; Yousef, Malik
    With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly -ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work's findings can guide effective design of new FS approaches using feature grouping.