Person: AĞAOĞLU, MUSTAFA
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AĞAOĞLU
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MUSTAFA
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Publication Metadata only Precision oncology: an ensembled machine learning approach to ıdentify a candidate mrna panel for stratification of patients with breast cancer(2022-08-01) AĞAOĞLU, MUSTAFA; ARĞA, KAZIM YALÇIN; Kurt, Fırat; Kurt F., AĞAOĞLU M., ARĞA K. Y.The rise of machine learning (ML) has recently buttressed the efforts for big data-driven precision oncology. This study used ensemble ML for precision oncology in breast cancer, which is one of the most common malignancies worldwide with marked heterogeneity of the underlying molecular mechanisms. We analyzed clinical and RNA-seq data from The Cancer Genome Atlas (TCGA) (844 patients with breast cancer and 113 healthy individuals) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (1784 patients with breast cancer and 202 healthy individuals). We evaluated six algorithms in the context of ensemble modeling and identified a candidate mRNA diagnostic panel that can differentiate patients from healthy controls, and stratify breast cancer into molecular subtypes. The ensemble model included 50 mRNAs and displayed 82.55% accuracy, 79.22% specificity, and 84.55% sensitivity in stratifying patients into molecular subtypes in TCGA cohort. Its performance was markedly higher, however, in distinguishing the basal, LumB, and Her2+ breast cancer subtypes from healthy individuals. In overall survival analysis, the mRNA panel showed a hazard ratio of 2.25 (p = 5 x 10(-7)) for breast cancer and was significantly associated with molecular pathways related to carcinogenesis. In conclusion, an ensemble ML approach, including 50 mRNAs, was able to stratify patients with different breast cancer subtypes and differentiate them from healthy individuals. Future prospective studies in large samples with deep phenotyping can help advance the ensemble ML approaches in breast cancer. Advanced ML methods such as ensemble learning are timely additions to the precision oncology research toolbox.Publication Metadata only Performance of simultaneous perturbation stochastic approximation for feature selection(2022-01-01) ALKAYA, ALİ FUAT; AĞAOĞLU, MUSTAFA; Algin R., ALKAYA A. F. , AĞAOĞLU M.© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Feature Selection (FS) is an important process in the field of machine learning where complex and large-size datasets are available. By extracting unnecessary properties from the datasets, FS reduces the size of datasets and evaluation time of algorithms and also improves the performance of classification algorithms. The main purpose of the FS is achieving a minimal feature subset from the initial features of the given problem dataset where the minimal feature subset should show an acceptable performance in representing the original dataset. In this study, to generate subsets we used simultaneous perturbation stochastic approximation (SPSA), migrating birds optimization and simulated annealing algorithms. Subsets generated by the algorithms are evaluated by using correlation-based FS and performance of the algorithms is measured by using decision tree (C4.5) as a classifier. To our knowledge, SPSA algorithm is applied to the FS problem as a filter approach for the first time. We present the computational experiments conducted on the 15 datasets taken from UCI machine learning repository. Our results show that SPSA algorithm outperforms other algorithms in terms of accuracy values. Another point is that, all algorithms reduce the number of features by more than 50%.Publication Open Access The effect of ERP implementation CSFs on business performance: an empirical study on users' perception(ELSEVIER SCIENCE BV, 2015-12) YURTKORU, EMİNE SERRA; Agaoglu, Mustafa; Yurtkoru, E. Serra; Ekmekci, Asli Kucukaslan; Zehir, C; Ozdemir, EEThis study is conducted on 220 employees involved in ERP implementation project in a multinational consumer goods company to investigate the CSFs and their effect on ERP implementation success from users' point of view. Findings indicate there are differences in which CSFs are perceived important and actually have effect on ERP implementation success. Interestingly, none of the ERP adopting organization environment CSFs had influence on project success. Nevertheless, 'careful selection of ERP software', 'software analysis, testing and troubleshooting', and 'vendor support' explain ERP project outcomes.Publication Open Access Medical Appointment No-Show Prediction Using Machine Learning Techniques(2022-01-01) AĞAOĞLU, MUSTAFA; Abushaaban E., AĞAOĞLU M.© 2022 IEEE.Health care resources are limited and the efficient utilization of these resources is a must. No show for medical appointments is an everlasting problem that faces health care systems and is a huge waste of resources that can be a hindrance against improving health care services all over the world. This research aims to analyze the factors affecting this problem and create effective predictive models that can help solve it and reduce its social and economical ramifications on health care systems. This study suggests a methodology based on machine learning where different algorithms were utilized and compared and proposes a framework that can be utilized to achieve the best, and most robust classifier in terms of different performance metrics. The objective is to attempt to predict if a patient will attend his/her medical appointment or not.