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12411 |
Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit
Enthalten in Scientific reports Bd. 9, 11.3.2019, Nr. 1, date:12.2019: 1-18
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12412 |
Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device
Enthalten in BMC medical informatics and decision making Bd. 22, 6.7.2022, Nr. 1, date:12.2022: 1-12
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12413 |
Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T
Enthalten in Scientific reports Bd. 13, 15.5.2023, Nr. 1, date:12.2023: 1-17
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12414 |
USE OF MULTIVARIATE MACHINE LEARNING ANALYSIS TECHNIQUES FOR FLOOD RISK PREVENTION
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLII-3/W4, 2018: 549-554
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12415 |
Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models
Enthalten in Journal of cheminformatics Bd. 3, 7.2.2011, Nr. 1, date:12.2011: 1-12
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12416 |
Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa
Enthalten in BMC infectious diseases Bd. 23, 19.7.2023, Nr. 1, date:12.2023: 1-13
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12417 |
Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study
Enthalten in Systematic Reviews Bd. 12, 15.4.2023, Nr. 1, date:12.2023: 1-14
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12418 |
User training for machine learning controlled upper limb prostheses: a serious game approach
Enthalten in Journal of neuroEngineering and rehabilitation Bd. 18, 12.2.2021, Nr. 1, date:12.2021: 1-15
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12419 |
Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling
Enthalten in BMC medical research methodology Bd. 22, 23.11.2022, Nr. 1, date:12.2022: 1-10
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12420 |
Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma
Enthalten in BMC cancer Bd. 21, 9.8.2021, Nr. 1, date:12.2021: 1-11
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