WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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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.
Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning
(IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, Burcu
The rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.
In-silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019-04) Ersoz, Nur Sebnem; Guzel, Yasin; Bakir-Gungor, Burcu
Representing approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified.
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.
Citation - WoS: 1
Citation - Scopus: 1
Textnettopics-SFTS-SBTS Textnettopics Scoring Approaches Based Sequential Forward and Backward
(Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
TextNetTopics is a text classification-based topic modeling approach that performs topic selection rather than word selection to train a machine learning algorithm. However, one main limitation of TextNetTopics is that its scoring component (the S component) assesses each topic independently and ranks them accordingly, neglecting the potential relationship between topics. In order to address this limitation and improve the classification performance, this study introduces an enhancement to TextNetTopics. TextNetTopics-SFTS-SBTS integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. This integration aims to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained across three datasets offer valuable insights into the context-dependent effectiveness of the new scoring mechanisms across diverse datasets and varying numbers of topics involved in the analysis.
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.
Citation - Scopus: 1
Semant - Feature Group Selection Utilizing Fasttext-Based Semantic Word Grouping, Scoring, and Modeling Approach for Text Classification
(Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, Malik
Text classification presents a challenge due to its high-dimensional feature space. As such, devising an effective feature selection scheme is essential. In this study, we present SEMANT, a novel hybrid filter-wrapper feature selection method that utilizes filter-based Chi-Square and the wrapper-based G-S-M approach. SEMANT incorporates fastText neural word embedding similarities to promote greater semantic inclusion in the selection of features for text classification tasks. The performance of the proposed method was investigated on the WOS-5736 and LitCovid datasets and compared with TextNetTopics, a topic modeling-based topic selection algorithm for text classification. Experimental results confirm that the proposed approach outperforms its alternative.
Citation - WoS: 8
Citation - Scopus: 12
SVM-RCE-R Optimization of Scoring Function for SVM-RCE
(Springer International Publishing AG, 2021) Yousef, Malik; Jabeer, Amhar; Bakir-Gungor, Burcu
Gene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.
Citation - WoS: 1
Citation - Scopus: 1
Prediction of Type 2 Diabetes Using Metagenomic Data and Identification of Taxonomic Biomarkers
(IEEE, 2024-05-15) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, Burcu
Nowadays, different molecular levels of -omics data on diseases are generated and analyzing these data with machine learning methods is one of the popular research topics. Among these data, the use of metagenomic data to facilitate the diagnosis, detection and treatment of diseases is increasing day by day. Type 2 diabetes (T2D) is a chronic disease characterized by insulin resistance and progressive dysfunction of pancreatic beta cells. While the number of people with diabetes is increasing by around 8% annually, the cost of treating the disease is rising by 18% per year. Therefore, the number of studies on the diagnosis, development and progression of T2D is increasing over time. The aim of this study is to achieve higher machine learning performance by using fewer metagenomic features and to achieve better classification performance by reducing computational costs. In this study, we compare the performance of three different methods using T2D-related metagenomic data. First, the MetaPhlAn tool is used to calculate the taxonomic species and their relative abundances in each sample. The SVM-RCE, RCE-IFE and microBiomeGSM tools used in this study are methods that perform classification by grouping and scoring features and are known to work well on complex datasets. In this study, the best results were obtained with the RCE-IFE tool with an AUC of 0.72 with an average of 125 features information. In addition, key taxonomic species identified by these tools as associated with T2D are presented in comparison to the literature.
Citation - WoS: 24
Citation - Scopus: 27
PANOGA: a Web Server for Identification of SNP-Targeted Pathways From Genome-Wide Association Study Data
(Oxford Univ Press, 2014-01-11) Bakir-Gungor, Burcu; Egemen, Ece; Sezerman, Osman Ugur
Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.

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