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Öğe Author Correction: Post-activation performance enhancement effect of drop jump on long jump performance during competition (Scientific Reports, (2023), 13, 1, (16993), 10.1038/s41598-023-44075-w)(Nature Research, 2023) dos Santos Silva D.; Boullosa D.; Moura Pereira E.V.; de Jesus Alves M.D.; de Sousa Fernandes M.S.; Badicu G.; Yagin F.H.Correction to: Scientific Reports, published online 09 October 2023 In the original version of this Article Georgian Badicu was incorrectly affiliated with ‘Department of Physical Education, Federal University of Sergipe (UFS), São Cristóvão, Brazil’. Their correct affiliation is listed below. Department of Physical Education and Special Motricity, Faculty of Physical Education and Mountain Sports, Transilvania University of Braşov, 500068, Braşov, Romania. The original Article has been corrected. © 2023, The Author(s).Öğe An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software(Institute of Electrical and Electronics Engineers Inc., 2019) Percin I.; Yagin F.H.; Arslan A.K.; Colak C.Classification analysis is a frequently used approach in fields such as biomedical, bioinformatics, medical and engineering. In the field of health, it has become common to classify diseases based on risk factors by machine learning methods and to determine the effect sizes of these risk factors on the disease. There are many analysis tools used to guide researchers in classification analysis. While some of these tools are commercial and provide basic methods for classification analysis, some offer advanced analysis techniques and are desktop applications such as the WEKA environment.The WEKA environment includes comprehensive tools for classification analysis. However, use of the WEKA environment can be difficult and time-consuming, especially when a quick assessment is essential for users who do not have WEKA tool on their computer (doctors, etc.). Therefore; fast, comprehensive, free and easy to use analysis tool is required. The purpose of this study is to develop a user-friendly web tool (Data Classification Software; DCS) based on the classification algorithms of WEKA tool in Java programming language.The data classification software can be used on any device with an internet connection, which is independent of the any operating systems. In the developed web-based tool, data preprocessing module consists of missing value assignment, variable type conversion and normalization-standardization methods. Classification module encapsulates random forest, Naive Bayes, Bayes Network, j48, sequential minimal optimization, a rule and attribute selected classifier algorithms. This web tool can be accessed free of charge at http://biostatapps.inonu.edu.tr/DCS/. © 2019 IEEE.Öğe INTERPRETABLE ESTIMATION OF SUICIDE RISK AND SEVERITY FROM COMPLETE BLOOD COUNT PARAMETERS WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS(Medicinska Naklada Zagreb, 2023) Cansel N.; Yagin F.H.; Akan M.; Aygul B.I.Background: The peripheral inflammatory markers are important in the pathophysiology of suicidal behavior. However, methods for practical uses haven’t been developed enough yet. This study developed predictive models based on explainable artificial intelligence (xAI) that use the relationship between complete blood count (CBC) values and suicide risk and severity of suicide attempt. Subjects and methods: 544 patients who attempted an incomplete suicide between 2010-2020 and 458 healthy individuals were selected. The data were obtained from the electronic registration systems. To develop prediction models using CBC values, the data were grouped in two different ways as suicidal/healthy and attempted/non-attempted violent suicide. The data sets were balanced for the reliability of the results of the machine learning (ML) models. Then, the data was divided into two; 80% of as the training set and 20% as the test set. For suicide prediction, models were created with Random Forest, Logistic Regression, Support vector machines and XGBoost algorithms. SHAP, was used to explain the optimal model. Results: Of the four ML methods applied to CBC data, the best-performing model for predicting both suicide risk and suicide severity was the XGBoost model. This model predicted suicidal behavior with an accuracy of 0.83 (0.78-0.88) and the severity of suicide attempt with an accuracy of 0.943 (0.91-0.976). Lower levels of NEU, WBC, MO, NLR, MLR and, age higher levels of HCT, PLR, PLT, HGB, RBC, EO, MPV and, BA contributed positively to the predictive created model for suicide risk, while lower PLT, BA, PLR and RBC levels and higher MO, EO, HCT, LY, MLR, NEU, NLR, WBC, HGB and, age levels have a positive contribution to the predictive created model for violent suicide attempt. Conclusion: Our study suggests that the xAI model developed using CBC values may be useful in detecting the risk and severity of suicide in the clinic. © Medicinska naklada, Zagreb & School of Medicine, University of Zagreb & Pro mente, Zagreb, Croatia.Öğe Machine learning approaches for multi-omics data integration in medicine(Springer International Publishing, 2023) Yagin F.H.Cells are a fundamental unit of life, and the ability to study the phenotypes and behavior of cells is crucial to understanding the functioning of complex biological systems. The prognostic and predictive accuracy of disease phenotypes can be enhanced by the use of integrative omics approaches due to their ability to examine biological processes holistically, which could lead to improved treatment and prevention in the long term. Therefore, multi-omics data integration strategies are needed to combine the complementary information brought by each omic layer. A major challenge in multi-omics research for disease diagnosis, monitoring, and treatment options is how to integrate high-dimensional data from omics. This chapter focused on machine learning methods for multi-omics data integration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. All rights reserved.Öğe Moderate aerobic training counterbalances the deleterious effect of undernutrition on oxidative balance and mitochondrial markers(Nature Research, 2024) de Sousa Fernandes M.S.; da Silva Pedroza A.A.; Martins Silva D.G.; de Andrade Silva S.C.; Pereira A.R.; Fernandes M.P.; Yagin F.H.The state of Maternal Protein Malnutrition (MPM) is associated with several deleterious effects, including inflammatory processes and dysregulation in oxidative balance, which can promote neurodegeneration. On the other hand, it is known that aerobic exercise can promote systemic health benefits, combating numerous chronic diseases. Therefore, we evaluate the effect of aerobic exercise training (AET) on indicators of mitochondrial bioenergetics, oxidative balance, endoplasmic reticulum stress, and neurotrophic factor in the prefrontal cortex of malnourished juvenile Wistar rats. Pregnant Wistar rats were fed with a diet containing 17% or 8% casein during pregnancy and lactation. At 30 days of life, male offspring were divided into 4 groups: Low-Protein Control (LS), Low-Protein Trained (LT), Normoprotein Control (NS), and Normoprotein Trained (NT). The trained groups performed an AET for 4 weeks, 5 days a week, 1 h a day per session. At 60 days of life, the animals were sacrificed and the skeletal muscle, and prefrontal cortex (PFC) were removed to evaluate the oxidative metabolism markers and gene expression of ATF-6, GRP78, PERK and BDNF. Our results showed that MPM impairs oxidative metabolism associated with higher oxidative and reticulum stress. However, AET restored the levels of indicators of mitochondrial bioenergetics, in addition to promoting resilience to cellular stress. AET at moderate intensity for 4 weeks in young Wistar rats can act as a non-pharmacological intervention in fighting against the deleterious effects of a protein-restricted maternal diet. © The Author(s) 2024.
