Economics, Econometrics and Finance, Finance, Artificial Intelligence, Statistics and Probability
4
Scopus Publications
5
Scholar Citations
2
Scholar h-index
Scopus Publications
Association of Serum Calprotectin and the C-Reactive Protein–Triglyceride–Glucose Index with SYNTAX Score in Patients with Newly Diagnosed Coronary Artery Disease Vahit Demir, Hüseyin Ede, Yaşar Turan, Muhammed Raşid Bakir, Çaglar Alp, Murat Gül, Halil Aktaş, Münire Işlak Demir, Oğuz Yıldırım, Sinan Inci Medicina Lithuania, 2026 Background and Objectives: Systemic inflammation is a key driver in the progression and complexity of coronary artery disease (CAD). Serum calprotectin and the C-reactive protein–triglyceride–glucose index (CTI) have emerged as potential inflammatory and metabolic biomarkers; however, their association with angiographic disease severity has not been clearly defined. This study aimed to evaluate the relationship between serum calprotectin, CTI, and the SYNTAX score (SS) in patients with stable CAD. Materials and Methods: A total of 134 patients undergoing coronary angiography were enrolled. The SS was calculated to quantify coronary lesion complexity. Patients were classified into two groups based on the results of the coronary angiogram: low SS (n = 73, SS < 23), and intermediate–high SS (n = 61, SS ≥ 23). Serum calprotectin, and CTI were obtained at baseline. Correlation analyses were performed to evaluate associations between biomarkers and SS. Receiver operating characteristic (ROC) curve analysis assessed the ability of these biomarkers to predict intermediate–high SS. Univariable and multivariable logistic regression analyses were performed to determine independent associations. Results: Patients with intermediate–high SS had significantly higher levels of serum calprotectin (1009.5 vs. 505.7 ng/mL), and CTI (9.9 vs. 9.5) compared with those with low SS (all p < 0.001). Spearman correlation analysis demonstrated significant positive correlations between SS and, serum calprotectin (ρ = 0.488), and CTI (ρ = 0.453) (all p < 0.001). ROC analysis showed moderate association in respect to intermediate–high SS (0.739 for serum calprotectin, and 0.722 for CTI). In multivariable models, CTI showed the strongest independent association with intermediate–high SS (OR: 4.66, 95% CI: 2.00–10.84, p < 0.001). Conclusions: Serum calprotectin and CTI were significantly associated with coronary lesion complexity, as measured by the SS. These biomarkers may serve as valuable tools for identifying patients with greater CAD severity and anatomical complexity.
Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye Muhammed Raşid Bakır, Mümin Atalay Çetin, İbrahim Bakırtaş Borsa Istanbul Review, 2025 This study revisits the macroeconomic determinants of non-performing loans using a deep neural network (DNN). We present the proposed DNN as a methodological framework that combines deep learning techniques and causal inference methods. We employ a rigorous triple-validation methodology that integrates deep learning architecture, random forest analysis, and the DoWhy causal inference framework. Furthermore, our optimized deep learning framework is validated and enhanced with causal inference capabilities, thus establishing a robust analytical framework for credit risk assessment in emerging markets. Although traditional analyses emphasize unemployment and debt stock as primary predictors, our causal inference methodology indicates that foreign direct investment exhibits the most substantial risk-mitigating effect. Real interest rates had substantial risk-mitigating effects compared with policy rates, suggesting the potential limitations of real interest rates in current monetary policy transmission mechanisms. The integration of deep learning and causal inference has significant implications for policy formulation, suggesting the efficacy of structural reforms over conventional monetary interventions.
Growth-Maximising Budget Deficit in BRICS-T: A Panel Threshold Approach BAKIRTAŞ İbrahim, BULUŞ Gökay Canberk, BAKIR Muhammed Raşid Economic Computation and Economic Cybernetics Studies and Research, 2024 The budget deficit affects the sustainability of macroeconomic stability for both developed and developing countries, although the reasons for their occurrence differ.The importance of budget deficit varies depending on whether it supports or hinders economic growth.This paper aims to estimate the growth-maximising budget deficit ratio in BRICS-T countries empirically.To calculate the growth-maximising budget deficit ratio, the panel threshold approach was used from 1990-2021.According to findings, the BRICS-T countries can maximise economic growth by keeping their budget deficit between 0.66% and 3.30% of GDP.The findings of this research point to the importance of fiscal discipline and support for moderate budget deficits for fiscal policymakers of BRICS-T countries.Moreover, it can be stated that the Maastricht Criteria has an extremely critical value not only for the European Union but also for the BRICS-T countries.
STATISTICAL TECHNIQUES VS. MACHINE LEARNING MODELS: A COMPARATIVE ANALYSIS FOR EXCHANGE RATE FORECASTING IN FRAGILE FIVE COUNTRIES BAKIR Muhammed Rasid, BAKIRTAS Ibrahim, OLMEZ Emre Economic Computation and Economic Cybernetics Studies and Research, 2023 In 2013, the Federal Reserve (Fed) announced the end of its expansionary monetary policy, which had a significant impact on certain countries.These countries, colloquially referred to as the "fragile five", were heavily dependent on financial capital flows, which led to deviations from inflation targets due to the exchange rate pass-through effect.Consequently, monetary authorities and other financial actors need accurate exchange rate forecasts to mitigate these deviations and improve the effectiveness of monetary policy.This study aims to forecast the exchange rates of the fragile five countries using both traditional statistical methods and machine learning techniques.The traditional statistical methods used in this study include Naïve Drift, Theta, Holt's Exponential Smoothing and ARIMA models, while the machine learning methods include RNN, LSTM, GRU and CNN architectures.The results show that machine learning methods outperform traditional statistical methods in terms of prediction accuracy for all countries.While statistical methods show a directional accuracy rate between 47% and 60%, RNN, one of the machine learning models, shows an accuracy rate between 80% and 90%.Overall, these results suggest that machine learning methods can provide more accurate exchange rate forecasts for the fragile five countries than traditional statistical methods.These findings may be valuable for monetary authorities and financial actors seeking to improve the effectiveness of monetary policy in these countries.
RECENT SCHOLAR PUBLICATIONS
Association of Serum Calprotectin and the C-Reactive Protein–Triglyceride–Glucose Index with SYNTAX Score in Patients with Newly Diagnosed Coronary Artery Disease V Demir, H Ede, Y Turan, MR Bakir, Ç Alp, M Gül, H Aktaş, MI Demir, ... Medicina 62 (5), 928 , 2026 2026.0
Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Turkiye MR Bakir, MA Cetin, I Bakirtas BORSA ISTANBUL REVIEW 25 (3), 541-551 , 2025 2025.0 Citations: 1
Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye M Raşid Bakır, M Atalay Çetin, İ Bakırtaş Borsa İstanbul Review 25 (3), 541-551 , 2025 2025.0
Growth-Maximising Budget Deficit in BRICS-T: A Panel Threshold Approach. İ BAKIRTAŞ, GC BULUŞ, MR BAKIR Economic Computation & Economic Cybernetics Studies & Research 58 (3) , 2024 2024.0
Döviz kuru tahminlemesinde geleneksel yöntemlere karşı makine öğrenmesi: kırılgan beşli ekonomileri için uygulamalar MR Bakır Aksaray Üniversitesi Sosyal Bilimler Enstitüsü , 2024 2024.0 Citations: 2
Statistical techniques vs. machine learning models: a comparative analysis for exchange rate forecasting in fragile five countries MR Bakır, İ Bakırtaş, E Ölmez Economic Computation and Economic Cybernetics Studies and Research 57 (3 … , 2023 2023.0
Can the Triglyceride/HDL Ratio in Chronic Kidney Disease be Predictive of Cardiac Risk? A Aktaş, MA GEDİKLİ, C KARA, MR BAKIR Age (year) 76, 5.39 , 2021 2021.0 Citations: 2
Makroekonomik değişkenlerin hisse senetleri fiyatları üzerindeki etkisinin ölçülmesi MR Bakir Gazi Üniversitesi Sosyal Bilimler Enstitüsü , 2016 2016.0
Kronik Böbrek Hastalığında Trigliserit/HDL Oranı Kardiyak Riskinin Öngördürücüsü Olabilir mi? A AKTAŞ, MA GEDİKLİ, C KARA, MR BAKIR
MOST CITED SCHOLAR PUBLICATIONS
Döviz kuru tahminlemesinde geleneksel yöntemlere karşı makine öğrenmesi: kırılgan beşli ekonomileri için uygulamalar MR Bakır Aksaray Üniversitesi Sosyal Bilimler Enstitüsü , 2024 2024.0 Citations: 2
Can the Triglyceride/HDL Ratio in Chronic Kidney Disease be Predictive of Cardiac Risk? A Aktaş, MA GEDİKLİ, C KARA, MR BAKIR Age (year) 76, 5.39 , 2021 2021.0 Citations: 2
Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Turkiye MR Bakir, MA Cetin, I Bakirtas BORSA ISTANBUL REVIEW 25 (3), 541-551 , 2025 2025.0 Citations: 1
Association of Serum Calprotectin and the C-Reactive Protein–Triglyceride–Glucose Index with SYNTAX Score in Patients with Newly Diagnosed Coronary Artery Disease V Demir, H Ede, Y Turan, MR Bakir, Ç Alp, M Gül, H Aktaş, MI Demir, ... Medicina 62 (5), 928 , 2026 2026.0
Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye M Raşid Bakır, M Atalay Çetin, İ Bakırtaş Borsa İstanbul Review 25 (3), 541-551 , 2025 2025.0
Growth-Maximising Budget Deficit in BRICS-T: A Panel Threshold Approach. İ BAKIRTAŞ, GC BULUŞ, MR BAKIR Economic Computation & Economic Cybernetics Studies & Research 58 (3) , 2024 2024.0
Statistical techniques vs. machine learning models: a comparative analysis for exchange rate forecasting in fragile five countries MR Bakır, İ Bakırtaş, E Ölmez Economic Computation and Economic Cybernetics Studies and Research 57 (3 … , 2023 2023.0
Makroekonomik değişkenlerin hisse senetleri fiyatları üzerindeki etkisinin ölçülmesi MR Bakir Gazi Üniversitesi Sosyal Bilimler Enstitüsü , 2016 2016.0
Kronik Böbrek Hastalığında Trigliserit/HDL Oranı Kardiyak Riskinin Öngördürücüsü Olabilir mi? A AKTAŞ, MA GEDİKLİ, C KARA, MR BAKIR
