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41511 - 41520 von 42118

	41511	Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets Enthalten in Archives of toxicology Bd. 97, 21.8.2023, Nr. 11, date:11.2023: 2969-2981
	41512	Unraveling the Most Important Predictors of Eudaimonic and Hedonic Well-Being in Korean Adults: A Machine Learning Approach Enthalten in Journal of happiness studies Bd. 25, 22.8.2024, Nr. 7, date:10.2024: 1-22
	41513	Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning approaches Enthalten in Discover oncology Bd. 16, 24.5.2025, Nr. 1, date:12.2025: 1-17
	41514	Unravelling individual rhythmic abilities using machine learning Enthalten in Scientific reports Bd. 14, 11.1.2024, Nr. 1, date:12.2024: 1-16
	41515	Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters Enthalten in BMC anesthesiology Bd. 24, 18.12.2024, Nr. 1, date:12.2024: 1-14
	41516	Unravelling rainfall trends: A comprehensive study of urban precipitation using innovative statistical and machine learning techniques in major cities of India Enthalten in Journal of earth system science Bd. 134, 23.7.2025, Nr. 3, date:9.2025: 1-11
	41517	Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression Enthalten in Nature Communications Bd. 16, 8.2.2025, Nr. 1, date:12.2025: 1-15
	41518	Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions Enthalten in npj science of food Bd. 9, 1.7.2025, Nr. 1, date:12.2025: 1-16
	41519	Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach Enthalten in Scientific reports Bd. 15, 29.7.2025, Nr. 1, date:12.2025: 1-26
	41520	Unsupervised Clustering for a Comparative Methodology of Machine Learning Models to Detect Domain-Generated Algorithms Based on an Alphanumeric Features Analysis Enthalten in Journal of network and systems management Bd. 32, 2.1.2024, Nr. 1, date:1.2024: 1-38