Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    A Machine Learning Model for Predicting Oligoclonal Band Positivity Using Routine Cerebrospinal Fluid and Serum Biochemical Markers
    (Oxford University Press Inc, 2025) Gözgöz, Hazar; Orhan, Oğuzhan; Akan Konuk, Başak; Akan, Pınar
    OBJECTIVE: To develop and validate a machine learning model for predicting oligoclonal band (OCB) positivity using routine cerebrospinal fluid (CSF) and serum biochemical markers to improve the diagnostic accuracy and efficiency of assessing intrathecal immunoglobulin G (IgG) synthesis. METHODS: In this retrospective study (n = 1709), an ensemble model was developed using 8 refined CSF and serum parameters. Combining optimized CatBoost, XGBoost, and LightGBM classifiers, the model was trained and evaluated using a 2-phase workflow, including 5-fold cross-validation and validation on independent internal (n = 342) and external (n = 49) cohorts. RESULTS: The developed ensemble model achieved a receiver operating characteristic-area under the curve (ROC-AUC) of 0.902 on the internal test set, significantly outperforming the conventional IgG index (ROC-AUC, 0.795). At its optimal threshold, the model demonstrated an accuracy of 0.830, with a sensitivity of 0.714 and a specificity of 0.916. On the external validation cohort, it achieved 90% accuracy and 96% sensitivity. CONCLUSIONS: A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation. © The Author(s) 2025.
  • Article
    Unveiling the Design Rules for Tunable Emission in Graphene Quantum Dots: A High-Throughput TDDFT and Machine Learning Perspective
    (Indian Acad Sciences, 2025) Ozonder, Sener; Ozdemir, Mustafa Coskun; Unlu, Caner
    The ability to tailor the optical properties of graphene quantum dots (GQDs) is critical for their application in optoelectronics, bioimaging and sensing. However, a comprehensive understanding of how shape, size and doping influence their emission properties remains elusive. In this study, we conduct a systematic high-throughput time-dependent density functional theory (TDDFT) and machine learning analysis of 284 distinct GQDs, varying in shape (square, hexagonal, amorphous), size (similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}1-2 nm) and doping configurations with elements B, N, O, S and P at varying concentrations (1.5-7%). Our findings reveal clear design principles for tuning emission wavelengths based on dopant type, concentration and GQD geometry. Notably, sulfur doping at specific concentrations consistently results in higher emission energies, with certain configurations yielding emissions within the visible range. By elucidating how quantum confinement effects, symmetry breaking and dopant-induced modifications govern GQD optical properties, we provide practical design rules for tailoring emission spectra for next-generation optoelectronic, bioimaging and sensing applications.