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.Article Citation - WoS: 6Citation - Scopus: 9Combining N-Grams and Graph Convolution for Text Classification(Elsevier, 2025-05) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanText classification, a cornerstone of natural language processing (NLP), finds applications in diverse areas, from sentiment analysis to topic categorization. While deep learning models have recently dominated the field, traditional n-gram-driven approaches often struggle to achieve comparable performance, particularly on large datasets. This gap largely stems from deep learning' s superior ability to capture contextual information through word embeddings. This paper explores a novel approach to leverage the often-overlooked power of n-gram features for enriching word representations and boosting text classification accuracy. We propose a method that transforms textual data into graph structures, utilizing discriminative n-gram series to establish long-range relationships between words. By training a graph convolution network on these graphs, we derive contextually enhanced word embeddings that encapsulate dependencies extending beyond local contexts. Our experiments demonstrate that integrating these enriched embeddings into an long-short term memory (LSTM) model for text classification leads to around 2% improvements in classification performance across diverse datasets. This achievement highlights the synergy of combining traditional n-gram features with graph-based deep learning techniques for building more powerful text classifiers.Article Citation - Scopus: 10Building a Challenging Medical Dataset for Comparative Evaluation of Classifier Capabilities(Elsevier Ltd, 2024-08) Bozkurt, Berat; Coskun, Kerem; Bakal, GokhanSince the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain. © 2024 Elsevier B.V., All rights reserved.