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27701 |
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Enthalten in BMC bioinformatics Bd. 9, 18.11.2008, Nr. 1, date:12.2008: 1-12
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27702 |
Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy
Enthalten in Supportive care in cancer Bd. 33, 13.2.2025, Nr. 3, date:3.2025: 1-10
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27703 |
Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes
Enthalten in Current diabetes reports Bd. 20, 3.12.2020, Nr. 12, date:12.2020: 1-19
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27704 |
Use of machine learning-based classification algorithms in the monitoring of Land Use and Land Cover practices in a hilly terrain
Enthalten in Environmental monitoring and assessment Bd. 196, 5.12.2023, Nr. 1, date:1.2024: 1-21
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27705 |
Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis
Enthalten in Molecular medicine Bd. 29, 20.3.2023, Nr. 1, date:12.2023: 1-16
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27706 |
Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates
Enthalten in Clinical pharmacokinetics Bd. 62, 10.6.2023, Nr. 8, date:8.2023: 1105-1116
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27707 |
Use of Machine Learning Methods to Analyze Patterns of Brain Activity during Assessment of the Self and Others
Enthalten in Neuroscience and behavioral physiology Bd. 53, 25.11.2023, Nr. 7, date:9.2023: 1210-1218
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27708 |
Use of Machine Learning Models for Classification of Myographic Diseases
Enthalten in Biomedical engineering Bd. 56, 12.1.2023, Nr. 5, date:1.2023: 353-357
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27709 |
Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population
Enthalten in BMC pulmonary medicine Bd. 22, 29.8.2022, Nr. 1, date:12.2022: 1-11
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27710 |
Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study
Enthalten in European journal of medical research Bd. 29, 3.1.2024, Nr. 1, date:12.2024: 1-9
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Administration
