|
821 |
Investigating ecosystem connections in the shelf sea environment using complex networks
Enthalten in Biogeosciences Bd. 21, 2024, Nr. 3: 731-746. 16 S.
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|
822 |
High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra
Enthalten in Biogeosciences Bd. 21, 2024, Nr. 2: 335-355. 21 S.
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823 |
Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea
Enthalten in Ocean science Bd. 20, 2024, Nr. 2: 417-432. 16 S.
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824 |
Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data
Enthalten in Ocean science Bd. 20, 2024, Nr. 1: 21-30. 10 S.
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825 |
Machine learning-guided strategies for reaction conditions design and optimization
Enthalten in Beilstein journal of organic chemistry Bd. 20, 2024: 2476-2492
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826 |
Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis
Enthalten in Beilstein journal of organic chemistry Bd. 20, 2024: 2280-2304
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827 |
Explainable Machine Learning Models to Predict Gibbs–Donnan Effect During Ultrafiltration and Diafiltration of High‐Concentration Monoclonal Antibody Formulations
Enthalten in Biotechnology journal Bd. 19, 2024, Nr. 10. 12 S.
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828 |
Comprehensive analysis based on the disulfidptosis-related genes identifies hub genes and immune infiltration for pancreatic adenocarcinoma
Enthalten in Open medicine Bd. 19, 2024, Nr. 1. 19 S.
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829 |
Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
Enthalten in Open medicine Bd. 19, 2024, Nr. 1. 17 S.
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|
830 |
Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis
Enthalten in Open medicine Bd. 19, 2024, Nr. 1. 12 S.
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