Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 4RCE-IFE: Recursive Cluster Elimination with Intra-Cluster Feature Elimination(PeerJ Inc., 2025-02-07) Kuzudisli, Cihan; Bakir-Gungor, Burcu; Qaqish, Bahjat; Yousef, MalikConference 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, MalikArticle G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration(MDPI, 2025-11-29) Unlu Yazici, Miray; Bakir-Gungor, Burcu; Yousef, MalikThe rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases.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, MalikIntegrating 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.Conference Object 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, BurcuThe 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.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, MalikConference Object Citation - WoS: 1Citation - Scopus: 1Textnettopics-SFTS-SBTS Textnettopics Scoring Approaches Based Sequential Forward and Backward(Springer International Publishing AG, 2024) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics 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.Conference Object Citation - Scopus: 1TextNetTopics_TIS: Enhancing Textnettopics With Random Forest-Based Topic Importance Scoring(Institute of Electrical and Electronics Engineers Inc., 2024-10-16) Voskergian, Daniel; Bakir-Güngör, Burcu; Yousef, MalikTextNetTopics is an innovative Latent Dirichlet Allocation-based topic selection method for training text classification models. One main limitation is its computationally intensive scoring mechanism, especially when applied to many topics. This scoring mechanism involves training a machine learning model (i.e., Random Forest) on each topic using the Monte-Carlo Cross-Validation approach and assigning a score value based on a specific performance metric (e.g., accuracy or F1-score). Moreover, the measured score does not account for the interactions between all features residing in all topics. This paper presents a new topic-scoring mechanism called Topic Importance Scoring. This computationally efficient approach trains a Random Forest model on all topics simultaneously and leverages the extracted feature importance values to give each topic a score reflecting its classification potential. The experiments on three diverse datasets confirm that the proposed method's performance is superior to the Topic Performance Scoring, which was used in the original TextNetTopics method. © 2024 Elsevier B.V., All rights reserved.Conference Object TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling(Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics 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.Conference Object Citation - Scopus: 1Semant - 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, MalikText 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.
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