Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Editorial Preface(Birkhauser, 2019) Inam, I.; Büyükaşık, E.Article Integrating QSAR Analysis and Machine Learning To Explore the Antidiabetic Potential of Natural Compounds(AMG Transcend Association, 2025) Sincar, B.; Yalcin, D.; Bayraktar, O.This study explores the antidiabetic potential of 72 natural compounds using molecular descriptors and QSAR modeling combined with machine learning techniques. The dataset includes 11 experimentally obtained compounds and 61 from the literature, characterized by their IC50 values indicating 50% inhibition of α-glucosidase enzyme activity. Molecular descriptors were generated using ChemAxon’s MarvinSketch and PADEL software, narrowing down over 3000 descriptors to 23 relevant features. Statistical analysis revealed significant multicollinearity among variables, necessitating the application of non-linear machine learning models, namely Random Forest and Gradient Boosting. These models demonstrated predictive capabilities with R² values of 0.7751 and 0.8066, respectively, and highlighted molecular weight and the number of heteroatoms in ring structures as critical features influencing IC50 values. Despite the dataset's variability and limited size, the study underscores the potential of integrating QSAR and machine learning approaches to effectively predict the antidiabetic activity of natural compounds. The findings provide valuable insights for advancing computational methods in drug discovery. © 2025 by the authors.Article Citation - Scopus: 2Digital Sensing Technologies in Cancer Care: a New Era in Early Detection and Personalized Diagnosis(Elsevier Ltd, 2025) Yucel, M.; Önder, A.; Kurt, T.; Keles, B.; Beyaz, M.; Karadağ, Y.; Yildiz, U.H.Digital sensor platforms are systems that integrate sensors with digital technology, which revolutionize data collection, processing, and transmission for enabling real-time, high-precision and automated diagnostics. These platforms often serve as the backbone of modern monitoring systems, enabling real-time data acquisition and analysis for a wide range of applications. Recent advancements in digital sensor platforms have paved the way for transformative innovations in cancer diagnosis. These cutting-edge technologies offer unprecedented opportunities to facilitate early detection, improve diagnostic accuracy, and personalize treatment methods. This review explores the landscape of digital sensor platforms in the context of cancer diagnosis, providing an overview of their principles, functionalities, and clinical applications. The review further illustrates that biosensors, lab-on-a-chip (LOC) devices and wearable sensors have leveraged on nanotechnology, biorecognition materials and artificial intelligence (AI) for revolutionizing cancer diagnosis. It consolidates the recent advances in digital sensor platforms for cancer diagnosis and the associated critical challenges, such as regulatory concerns, standardization, and ethical considerations. Further, the review summarizes the feasibility for the integration of digital sensor platforms with routine clinical practices for the development of efficient cancer diagnosis and treatment methods. © 2025 The AuthorsBook Part Controlled Release Kinetics(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Controlled Release Dose(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Controlled Release by Membrane(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Controlled Drug Release(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Urease Immobilization on Membranes(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Release of Antimicrobial Agent(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.Book Part Controlled Release(Springer Berlin Heidelberg, 2016) Altinkaya, S.A.