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Öğe A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence(Frontiers Media Sa, 2024) Atay, Feyza Firat; Yagin, Fatma Hilal; Colak, Cemil; Elkiran, Emin Tamer; Mansuri, Nasrin; Ahmad, Fuzail; Ardigo, Luca PaoloBackground Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.Methods The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.Results The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions.Conclusion This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.Öğe Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department(Bmc, 2025) Yagin, Fatma Hilal; Aygun, Umran; Colak, Cemil; Alkhalifa, Amal K.; Alzakari, Sarah A.; Aghaei, MohammadrezaBackgroundSepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial intelligence (XAI) technologies, to construct a predictive model that balances high accuracy and clinical interpretability for use in emergency departments (EDs) and to examine candidate biomarkers.MethodsThe study identified a significant class imbalance problem in the sepsis distribution among 560 sepsis and 1012 non-sepsis patients. To address the imbalance issue, SMOTE-NC was applied in the training data. The data was divided into two parts, 80% training and 20% testing. To ensure the reliability of the models and to report unbiased results, this process was repeated 100 times and the average performance was reported. To determine the best model for sepsis prediction, five different models (AdaBoost, Gradient Boosting, CatBoost, LightGBM, and EBM) were trained, and their performances were evaluated. In the last stage, we presented local and global explanations of EBM.ResultsThe EBM model achieved the highest success by reaching 79.1% F1-score, 80.9% sensitivity, and 84.8% AUC after resampling. In the global explanations, the variables with the highest weights in the model's decision process were identified as positive blood culture, oxygen saturation, and procalcitonin, respectively.ConclusionThe EBM model accurately predicts sepsis risk based on clinically relevant biomarkers. The model's high performance and inherent transparency can foster trust among clinicians and facilitate its integration into emergency department workflows for real-time decision support.Öğe Acute effect of hydrogen-rich water on physical, perceptual and cardiac responses during aerobic and anaerobic exercises: a randomized, placebo-controlled, double-blinded cross-over trial(Frontiers Media Sa, 2023) Jebabli, Nidhal; Ouerghi, Nejmeddine; Abassi, Wissal; Yagin, Fatma Hilal; Khlifi, Mariem; Boujabli, Manar; Bouassida, AnissaMolecular hydrogen (H2 gas) dissolved in water to produce Hydrogen-Rich Water. Hydrogen-Rich Water (HRW) is considered as ergogenic aid in different exercise modes. However, acute pre-exercise HRW ingestion effect is unclear regarding athlete performance. This study aimed at investigating acute effect of HRW ingestion on aerobic and anaerobic exercise performance. Twenty-two male amateur middle-distance runners volunteered to participate in this study. In a randomized, double-blind study design, all players ingested 500 mL of HRW or placebo (PLA) supplement 30 min before the start of the tests. Over 4 days, maximal aerobic speed of Vameval test (MAS), time to exhaustion at MAS (Tlim), squat jump (SJ), counter-movement jump (CMJ) and five jump test (5JT) were evaluated. Also, rate of perceived exertion (RPE) and peak heart rate (HRpeak) were measured during the aerobic tests. For Vameval test, HRW ingestion improved MAS, HRpeak and RPE compared with the placebo condition. For Tlim test, HRW ingestion demonstrated improvements in time to exhaustion, RPE and HRpeak. However, no significant change was observed between HW and placebo conditions in SJ, CMJ, 5JT. 500 mL of HRW can significantly improve HRpeak, time to exhaustion, RPE, with no significant effect on MAS, jumping performance in amateur endurance athletes.Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah AliIntroduction Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. Methods In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. Results White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusion The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study (vol 11, 1285067, 2024)(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah Ali[Abstract Not Available]Öğe Are there differences between Mediterranean diet and the consumption of harmful substances on quality of life?-an explanatory model in secondary education regarding gender(Frontiers Media Sa, 2023) Melguizo-Ibanez, Eduardo; Zurita-Ortega, Felix; Ubago-Jimenez, Jose Luis; Badicu, Georgian; Yagin, Fatma Hilal; Gonzalez-Valero, Gabriel; Ardigo, Luca PaoloBackgroundAdolescence is a key life stage in human development. It is during this stage of development that healthy and physical behaviors are acquired that will last into adulthood. Gender differences in the acquisition of these behaviors have been observed. This research aims to (a) study the levels of Mediterranean diet adherence, quality of life and alcohol and tobacco consumption as regarding the gender of the participants and (b) study the effects of the variable adherence to the Mediterranean diet, alcohol consumption and tobacco consumption on quality of life as a function of the gender of the participants.MethodsA non-experimental, cross-sectional, exploratory study was carried out in a sample of 1,057 Spanish adolescents (Average Age = 14.19; Standard Deviation = 2.87).ResultsThe comparative analysis shows that the male teenagers shows a higher Mediterranean diet adherence compared to the male adolescents (p <= 0.05) and a higher consumption of alcoholic beverages (p <= 0.05). On the contrary, adolescent girls show a higher consumption of alcoholic beverages than male participants (p <= 0.05). The exploratory analysis indicates that for boys, alcohol consumption has a beneficial effect on the quality of life of adolescents (beta = 0.904; p <= 0.001).ConclusionIn this case, participants show differences in the levels of Mediterranean diet adherence, consumption of harmful substances and quality of life according to gender. Likewise, there are different effects between the variables according to gender. Therefore, gender is a key factor to consider during adolescence.Öğe ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine(Ieee, 2019) Percin, Ibrahim; Yagin, Fatma Hilal; Guldogan, Emek; Yologlu, SaimIn this study, it is aimed to develop a user-friendly, interactive web software for association rules mining. In the developed software, among the association rule methods; Filtered Associatior, Apriori, Frequent Pattern Growth, Predictive Apriori, Generalized Sequential Patterns, HotSpot, Tertius algorithms are used. In addition, association rules algorithms have certain limitation(s) regarding the structure of the data set. Therefore, preprocess menu in the software includes missing value assignment and variable type conversion, methods. In order to evaluate the association rules, support and confidence criteria are present in the software. However, it is not always possible to distinguish interesting and important rules only according to criteria of support and confidence. Therefore, in the proposed software; leverage, lift and conviction criteria are also included. A medical application is performed by using association rules mining, and the experimental results are evaluated based on the outputs of the developed software.Öğe Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yilmaz, Rustem; Yagin, Fatma Hilal; Raza, Ali; Colak, Cemil; Akinci, Tahir CetinAcute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.Öğe Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model(Mdpi, 2024) Aygun, Umran; Yagin, Fatma Hilal; Yagin, Burak; Yasar, Seyma; Colak, Cemil; Ozkan, Ahmet Selim; Ardigo, Luca PaoloThis study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.Öğe Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence(Frontiers Media Sa, 2024) Yagin, Fatma Hilal; Gormez, Yasin; Al-Hashem, Fahaid; Ahmad, Irshad; Ahmad, Fuzail; Ardigo, Luca PaoloBackground Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.Methods Data obtained after targeted metabolomics analyses using plasma samples from BC patients (n = 102) and healthy controls (n = 99) were used. Machine learning (ML) models based on raw data were developed, then feature selection methods were applied, and the results were compared. SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.Results The results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. SHAP annotations of the LR model revealed that Leucine, isoleucine, L-alloisoleucine, norleucine, and homoserine acids were the most important potential BC diagnostic biomarkers. Combining the identified metabolite markers provided robust BC classification measures with precision, recall, and specificity of 89.50%, 88.38%, and 83.67%, respectively.Conclusion In conclusion, this study adds valuable information to the discovery of BC biomarkers and underscores the potential of targeted metabolomics-based diagnostic advances in the management of BC.Öğe Can Environmental Enrichment Modulate Epigenetic Processes in the Central Nervous System Under Adverse Environmental Conditions? A Systematic Review(Springer/Plenum Publishers, 2024) Fernandes, Matheus Santos de Sousa; Costa, Moara Rodrigues; Badicu, Georgian; Yagin, Fatma Hilal; Santos, Gabriela Carvalho Jurema; da Costa, Jonathan Manoel; de Souza, Raphael FabricioThe aim of this paper is to summarize the available evidence in the literature regarding the effects generated by exposure to an enriched environment (EE) on the modulation of epigenetic processes in the central nervous system under adverse environmental conditions. Searches were conducted in three databases: PubMed/Medline (1053 articles), Scopus (121 articles), and Embase (52 articles), which were subjected to eligibility criteria. Of the 1226 articles found, 173 duplicates were removed. After evaluating titles/abstracts, 904 studies were excluded, resulting in 49 articles, of which 14 were included in this systematic review. EE was performed using different inanimate objects. Adverse environmental conditions included CUMS, sepsis, nicotine exposure, PCP exposure, early stress, WAS, high fructose intake, TBI, and sevoflurane exposure. Regarding microRNA expression, after exposure to EE, an increase in the expression of miR-221 and miR-483 was observed in the prefrontal cortex, and a reduction in the expression of miR-92a-3p and miR-134 in the hippocampus. Regarding histone modifications, in the hippocampus, there was a reduction of HAT, HDAC/HDAC4, H3 (acetyl K14), H4 (acetyl K15), H3K4me3, K3k27me3, and HDAC2/3/5. In the cortex, there was a reduction of HDAC2, and in the prefrontal cortex, there was an increase in acetylated H3. Regarding DNA modifications, there was a reduction of DNMT in the hippocampus. This systematic review concludes that the benefits of EE on the brain and behavior of animals are directly related to different epigenetic mechanisms, reflecting in cell growth and neuroplasticity. EE may be a non-pharmacological and easy-to-apply alternative to prevent symptoms in disorders affecting brain tissue.Öğe Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research(Mdpi, 2023) Yagin, Burak; Yagin, Fatma Hilal; Colak, Cemil; Inceoglu, Feyza; Kadry, Seifedine; Kim, JungeunAim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the black box problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p <= 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p <= 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.Öğe Change of direction and linear speed relation to functional ability and joint mobility in Polish U19 volleyball and basketball 3 x 3 national teams(Frontiers Media Sa, 2024) Czyznielewska, Zuzanna; Gabrys, Tomasz; Yagin, Fatma Hilal; Cepicka, LadislavThe purpose of this study was to determine the extent of differences in the level of change in linear speed and velocity in the modified change of direction test (COD) and to determine the relationship between speed deficits resulting from changes of direction and functional performance between groups of Polish U19 Volleyball National Team and Polish Women's Basketball 3 x 3 National Team. A total of 23 athletes: 12 volleyball players (age: 18 +/- 0 years; body height: 183 +/- 7 cm; body weight: 70 +/- 8 kg) and 11 basketball players (age: 26 +/- 4 years; body height: 180 +/- 6 cm; body weight: 73 +/- 10 kg) participated in the study. Athletes were tested for the following measures: Functional Movement Screen test (FMS), dynamic balance test Y-Balance, joints range of motion measurements, maximal sprint test (14 m), modified COD test (14 m) and change of direction deficit (CODD). A value of p < 0.05 was considered statistically significant. There was no significant correlation between sprint and CODD results in basketball team. In volleyball team there was a positive and significant correlation between COD, sprint and CODD. There was a negative and significant correlation between Y-Balance scores and sprint test results in the basketball team. Basketball team had a positive significant correlation between hip rotations and COD results. There was a negative significant correlation between shoulder movements and COD and CODD results in volleyball team.Öğe Chronic Disease Management of Children Followed with Type 1 Diabetes Mellitus(Galenos Publ House, 2023) Baysal, Senay Gueven; Ciftci, Nurdan; Duendar, Ismail; Bueyuekavci, Mehmet Akif; Yagin, Fatma Hilal; Camtosun, Emine; Dogan, Derya GuemuesObjective: With the diagnosis of chronic illness in children, a stressful period is likely to begin for both the affected child and their families. The aim of this study was to investigate the factors affecting chronic disease management by the parents of children diagnosed with type 1 diabetes mellitus (T1DM).Methods: The sample consisted of 110 children, aged between 4-17 years and their mothers. The patients had been diagnosed with T1DM for at least one year, and had attended pediatric endocrinology outpatients or were hospitalized in a single center. First, sociodemographic information about the child with T1DM were obtained. Then, the Family Management Measure (FaMM) was applied. The FaMM is constructed to measure family functioning and management in families who have a child with a chronic illness.Results: Paternal years of education (p=0.036), family income (p=0.008), insulin pump use (p=0.011), and time elapsed after diagnosis (p=0.048) positively affected both the management of T1DM and the child's daily life. However, presence of chronic diseases in addition to T1DM (p=0.004) negatively affected diabetes management. Higher maternal education year (p=0.013) and family income level (p=0.001) increased parental mutuality scores. However, as the time after diagnosis increased, parental mutuality scores decreased.Conclusion: It is important to evaluate the child with chronic disease with a biopsychosocial approach. This approach aims to evaluate the problems of the child and his/her family who experience the disease with a holistic approach.Öğe Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease(Frontiers Media Sa, 2023) Edache, Emmanuel Israel; Uzairu, Adamu; Mamza, Paul Andrew; Shallangwa, Gideon Adamu; Yagin, Fatma Hilal; Samee, Nagwan Abdel; Mahmoud, Noha F.The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo & BOTTOM;2 = 0.5548, Q_lto & BOTTOM;2 = 0.5278, R & BOTTOM;2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo & BOTTOM;2 = 0.7033, Q_lto & BOTTOM;2 = 0.6827, Q_lmo & BOTTOM;2 = 0.6305, R & BOTTOM;2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.Öğe Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction(Mdpi, 2024) Arslan, Ahmet Kadir; Yagin, Fatma Hilal; Algarni, Abdulmohsen; AL-Hashem, Fahaid; Ardigo, Luca PaoloAcute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.Öğe A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images(Duzce Univ, Fac Medicine, 2021) Yagin, Fatma Hilal; Guldogan, Emek; Ucuzal, Hasan; Colak, CemilObjective: Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19. Methods: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden's Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes. Results: The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden's index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research. Conclusions: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/..Öğe Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome(Peerj Inc, 2024) Yagin, Fatma Hilal; Shateri, Ahmadreza; Nasiri, Hamid; Yagin, Burak; Colak, Cemil; Alghannam, Abdullah F.Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.Öğe Developmental characteristics of Williams-Beuren syndrome and evaluation of adaptive behavioral skills(Tubitak Scientific & Technological Research Council Turkey, 2023) Guven Baysal, Senay; Arslan, Feyzullah Necati; Buyukavci, Mehmet Akif; Yagin, Fatma Hilal; Ekici, Cemal; Esener, Zeynep; Gumus Dogan, DeryaBackground/aim: Williams-Beuren syndrome (WBS) is a rare genetic disorder with delays in language and cognitive development, but, with increased awareness of clinical features and a reliable diagnostic test, WBS is becoming more widely recognized in childhood. Adaptive behavior skills and/or maladaptive behavior are important for the prognosis of individuals with WBS. The aim of this study was to investigate the clinical and developmental characteristics of patients with WBS and further increase awareness about it by evaluating the adaptive skills and maladaptive behaviors of the patients.Materials and methods: The data of WBS patients followed-up at the Developmental Behavioral Pediatrics Unit were reviewed. Patient data on perinatal and postnatal history, developmental stages, physical and neurological examination findings were collected. The International Guide for Monitoring Child Development (GMCD) was administered to each child. In addition, semistructured interviews were conducted with the parents using the Vineland Adaptive Behavior Scales, Second edition (Vineland-II).Results: A total of 12 patients diagnosed with WBS via detection of the 7q11.23 deletion, of whom 6 were girls, were retrospectively reviewed. The mean age at the time of review was 54.6 +/- 32.5 months. The mean age at first presentation to the Developmental Behavioral Pediatrics Outpatient Clinic was 15 +/- 11.5 months. In the first developmental evaluation using the GMCD, there was a delay in fine and gross motor domains in 6 patients, in the language domains in 4 patients, and in all of the domains in 2 patients. Findings with Vineland-II showed socialization and communication domains as strengths, but the daily living skills and motor skills domains were weaknesses. In terms of maladaptive behavior, the patients tended to frequently have behavioral problems, neurodevelopmental disease, anxiety disorders, eating problems, and sleeping problems.Conclusion: This retrospective review of 12 patients indicated a general delay in overall development, and confirmed impairment in both adaptive and maladaptive functioning in WBS.Öğe Diagnostic Performance of Ultrasound-Based Liver Fat Quantification With Reference to Magnetic Resonance Imaging Proton Density Fat Fraction and Histology(Wiley, 2025) Dag, Nurullah; Sarici, Baris; Igci, Gulnur; Yagin, Fatma Hilal; Yilmaz, Sezai; Kutlu, RamazanPurpose: To investigate the diagnostic performance of ultrasound attenuation imaging technology (USAT) in the evaluation of hepatic steatosis using magnetic resonance imaging proton density fat fraction (MRI PDFF) and histology as reference standards. Methods: In this single-center, prospective study, the liver fat content of 117 potential liver donor candidates was assessed by USAT and MRI PDFF between April and August 2024. Intraoperative liver biopsy was performed in 47 liver donors. Cut-off values of 6%, 17%, 22%, and 5%, 33%, 66% were used for mild, moderate, and severe steatosis in MRI PDFF and histology, respectively. The correlation between USAT and MRI PDFF was evaluated using Spearman's rho technique. Receiver operating characteristic (ROC) analysis was performed for the diagnostic performance of USAT, and optimal USAT cut-off values for different grades of hepatosteatosis were obtained. Results: There was a very strong correlation between USAT and MRI PDFF (rho = 0.933, p < 0.001). For MRI PDFF values greater than 6%, the area under the curve (AUC) was 0.97 [95% confidence interval (CI): 0.93-0.99] (p < 0.001). USAT cut-off values for differentiating between different grades of liver steatosis were 0.57, 0.68, and 0.76 dB/cm/MHz for mild, moderate, and severe steatosis, with sensitivities of 88.9%, 90.0%, and 86.7%, respectively. For histologically confirmed steatosis greater than 5%, the AUC was 0.94 (95% CI: 0.83-0.99) (p < 0.001), with a cut-off of 0.56 dB/cm/MHz for 84.6% sensitivity. Conclusion: USAT demonstrates excellent diagnostic accuracy in both the quantification and grading of hepatic steatosis.
