Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article AI-Driven Drug Repositioning: A Diffusion Model Approach on Knowledge Graphs(Elsevier, 2026) Erkantarci, Betul; Şen, Tarık Üveys; Bakal, GokhanDrug repositioning - discovering new therapeutic applications for existing drugs - offers a promising pathway to accelerate cancer treatment development. This study proposes a diffusion model-driven framework that leverages biomedical knowledge graphs and graph-based learning to enhance drug repositioning predictions. The framework integrates data from the Semantic MEDLINE Database (SemMedDB), the Unified Medical Language System (UMLS), and the Repurposing Drugs Database (RepoDB) to construct a comprehensive therapeutic knowledge graph. Drug embeddings are generated using a one-layer Relational Graph Convolutional Network (R-GCN) incorporating semantic type-guided structural perturbations. These embeddings are refined through a flow-matching algorithm to denoise and reconstruct biologically meaningful representations. To evaluate the model's effectiveness, we apply a consensus strategy using Cosine Similarity, Euclidean Distance, and Manhattan Distance as proximity metrics. The model successfully identified, on average, 74 candidate drugs for repositioning in the context of leukemia. Qualitative analysis using t-distributed stochastic neighbor embedding (t-SNE) revealed enhanced clustering of pharmacologically relevant drugs in the denoised embedding space. Trastuzumab, in particular, emerged as a strong repositioning candidate for leukemia, supported by 156 co-mentions in PubMed. These findings demonstrate that the proposed framework improves embedding robustness and semantic fidelity, offering a powerful artificial intelligence (AI)-driven approach for precision oncology. Integrating structural noise modeling with diffusion-based denoising advances the discovery of novel drug-disease associations and holds potential for translational research and clinical hypothesis generation in drug repurposing.Conference Object Citation - Scopus: 1Semantic-Forward Relaying for 6G: Performance Boosts With ResNet-18 and GoogleNet Plus(Institute of Electrical and Electronics Engineers Inc., 2024-11-28) Erkantarci, Betul; Çoban, Mert Korkut; Bozoǧlu, Abdulkadir; Köse, AbdulkadirThis paper investigates the integration of advanced deep learning architectures, namely ResNet-18, GoogleNet and enhanced GoogleNet (GoogleNet Plus), into the Semantic-Forward (SF) relaying framework for cooperative communications in 6G networks. The SF relaying framework enhances transmission efficiency and robustness by leveraging semantic information at relay nodes. We analyze and compare the performance of these deep learning models in terms of validation accuracy, semantic accuracy, and Euclidean distance (ED) metrics on the CIFAR-10 dataset. Results indicate that ResNet-18 achieves the highest performance due to its residual learning architecture. GoogleNet Plus, incorporating Automatic Mixed Precision (AMP) training and the Adam optimizer, demonstrates improved stability and efficiency compared to the original GoogleNet. The results highlights the potential of deep learning models to enhance semantic processing capabilities in SF relaying, contributing to the development of more efficient, resilient, and adaptive cooperative communication systems in 6G networks. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 16Citation - Scopus: 11An Empirical Study of Sentiment Analysis Utilizing Machine Learning and Deep Learning Algorithms(Springernature, 2023-12-09) Erkantarci, Betul; Bakal, GokhanAmong text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73-76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.