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Fatma Hilal Yağın
Malatya Turgut Ozal University
18H指数
167论文数
1.4K被引数
收录论文 62
发表时间
A Generalizable and Interpretable Framework for Molecular Subtype Classification of Pancreatic Ductal Adenocarcinoma Integrating Conformal Uncertainty Quantification and Consensus-Based Explainable Artificial Intelligence Across Multiple Cohorts一种通用的且可解释的框架,用于整合跨多个队列的符合性不确定性量化和基于共识的可解释人工智能,对胰腺导管腺癌进行分子亚型分类
Leveraging Explainable Automated Machine Learning (AutoML) and Metabolomics for Robust Diagnosis and Pathophysiological Insights in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)Yagin, F.H.; Colak, C.; Al-Hashem, F.; Alzakari, S.A.; Alhussan, A.A.; Aghaei, M. 利用可解释的自动化机器学习(AutoML)和代谢组学实现肌痛性脑脊髓炎/慢性疲劳综合征(ME/CFS)的稳健诊断与病理生理学见解。Diagnostics 2025, 15, 2755. [Google Scholar] [CrossRef] [PubMed]
Diagnostics
IF3.3

