Yazar "Cingiz, Mustafa Ozgur" seçeneğine göre listele

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    LBSA-DRIVER: A Novel Approach to Identify Cancer Driver Genes Using List-Based Simulated Annealing
    (Bentham Science Publ Ltd, 2025) Atay, Yilmaz; Ngobesing, Lionel Alangeh; Cingiz, Mustafa Ozgur
    Introduction Cancer driver genes are genes responsible for cancer genesis; thus, identifying cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer driver genes within the vast pool of normal passenger genes directly influences the efficacy of treatment approaches.Objective This research aimed to effectively identify cancer driver genes using the List-based Simulated Annealing (LBSA) optimization technique.Method The proposed model (LBSA-DRIVER) harnesses a list-based simulated annealing algorithm within a bipartite network to pinpoint cancer driver genes. The process begins with creating a bipartite graph that integrates gene mutations and expression data from carefully chosen datasets. The LBSA algorithm is then applied to the generated graph to identify and rank the genes, drawing insights from a biological interaction network.Result Following the algorithm's development, rigorous experimental analyses have been conducted using four benchmark datasets from The Cancer Genome Atlas (TCGA) database. The datasets used were the Breast Cancer dataset (BRCA), Prostate Adenocarcinoma dataset (PRAD), Ovarian cancer dataset (OV), and Glioblastoma Multiforme dataset (GBM).Conclusion Our findings, including precision, recall, F-score, and accuracy metrics, provide strong evidence of the effectiveness of the proposed model in identifying driver genes.
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    Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
    (Mdpi, 2025) Sezik, Savas; Cingiz, Mustafa Ozgur; Ibis, Esma
    With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can help solve clinical issues. The aim of this study was to construct seven machine learning models to predict the outcomes of emergency department patients and compare their prediction performance. Data from 75,803 visits to the emergency department of a public hospital between January 2022 to December 2023 were retrospectively collected. The final dataset incorporated 34 predictors, including two sociodemographic factors, 23 laboratory variables, five initial vital signs, and four emergency department-related variables. They were used to predict the outcomes (mortality, referral, discharge, and hospitalization). During the study period, 316 (0.4%) visits ended in mortality, 5285 (7%) in referral, 13,317 (17%) in hospitalization, and 56,885 (75%) in discharge. The disposition accuracy (sensitivity and specificity) was evaluated using 34 variables for seven machine learning tools according to the area under the curve (AUC). The AUC scores were 0.768, 0.694, 0.829, 0.879, 0.892, 0.923, and 0.958 for Adaboost, logistic regression, K-nearest neighbor, LightGBM, CatBoost, XGBoost, and Random Forest (RF) models, respectively. The machine learning models, especially the discrimination ability of the RF model, were much more reliable in predicting the clinical outcomes in the emergency department. XGBoost and CatBoost ranked second and third, respectively, following RF modeling.