Yazar "Ahmad, Irshad" seçeneğine göre listele

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    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 Paolo
    Background 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.
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    Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery
    (Mdpi, 2024) Yagin, Fatma Hilal; Al-Hashem, Fahaid; Ahmad, Irshad; Ahmad, Fuzail; Alkhateeb, Abedalrhman
    Background: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Methods: Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. Results: As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. Conclusions: ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.
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    The effects of French contrast method on explosive power and speed-related skills in female soccer players: a randomized controlled trial
    (Bmc, 2026) Aslan, Tahir Volkan; Aygun, Yalin; Tufekci, Sakir; Yagin, Fatma Hilal; Buyukcelebi, Hakan; Ahmad, Irshad; Ardigo, Luca Paolo
    Background The purpose of this study was to examine whether the addition of a 6-week French Contrast Method (FCM) to routine soccer training affects agility, vertical jump height and 30-m sprint performance in female soccer players. Methods A pretest-posttest control-group design was used. Twenty-four female soccer players (> 3 years of playing experience; regular training) participated. The experimental group performed a 6-week FCM program in addition to routine soccer training, while the control group continued routine soccer training only. Agility, vertical jump and 30-m sprint tests were administered at baseline and post-intervention. Within the scope of the study analyses, the primary outcomes (agility, vertical jump, and sprint performance) were derived from within-group pretest-posttest comparisons, whereas the secondary outcomes were obtained from findings related to the participants' descriptive characteristics. 2 Result The group x time interaction was found to be significant in the Illinois Agility Test ( F = 16.813 , p = 0.0004 eta rho & sup2; = 0.433). Performance time decreased from 18.44 +/- 0.59 s to 17.09 +/- 0.365 (Delta = - 1.35s) in the experimental group, while the change was limited in the control group (Delta = - 0.72s) ; the mean difference between groups was - 1.13 s (95% CI: -1.54 to-0.72). A significant group x time interaction was also detected in vertical jump performance ( F = 12.415 , p = 0.002 eta rho & sup2; = 0.361), with an increase of +3.31 cm in the experimental group and +0.77 cm in the control group (between-group difference: +2.54 cm; 95% CI:-0.64 to 5.72). In contrast, the group x time interaction was not significant for the 30 m sprint performance ( F = 0.869 , p = 0.361 eta rho & sup2; = 0,038). Conclusion These results indicate that the FCM training program is effective in improving female soccer players' agility and vertical jump performance, but does not create a significant difference between groups in 30 m sprint performance. These findings not only extend the scientific literature but also provide actionable strategies for coaches and practitioners.