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16071 - 16080 von 16790
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Artikel 16071 SVFX: a machine learning framework to quantify the pathogenicity of structural variants
Enthalten in Genome biology Bd. 21, 9.11.2020, Nr. 1, date:12.2020: 1-21
Online Ressource
Artikel 16072 SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants
Enthalten in Nature Communications Bd. 16, 11.3.2025, Nr. 1, date:12.2025: 1-14
Online Ressource
Artikel 16073 SVM directed machine learning classifier for human action recognition network
Enthalten in Scientific reports Bd. 15, 3.1.2025, Nr. 1, date:12.2025: 1-18
Online Ressource
Artikel 16074 SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections
Enthalten in GPS solutions Bd. 26, 8.6.2022, Nr. 3, date:7.2022: 1-14
Online Ressource
Artikel 16075 Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
Enthalten in Scientific reports Bd. 10, 11.12.2020, Nr. 1, date:12.2020: 1-11
Online Ressource
Artikel 16076 Symptom-based scoring technique by machine learning to predict COVID-19: a validation study
Enthalten in BMC infectious diseases Bd. 23, 12.12.2023, Nr. 1, date:12.2023: 1-11
Online Ressource
Artikel 16077 Symptom clusters among cancer survivors: what can machine learning techniques tell us?
Enthalten in BMC medical research methodology Bd. 21, 16.8.2021, Nr. 1, date:12.2021: 1-12
Online Ressource
Artikel 16078 Synergic quantum generative machine learning
Enthalten in Scientific reports Bd. 13, 9.8.2023, Nr. 1, date:12.2023: 1-14
Online Ressource
Artikel 16079 Synergistic bioinformatics and sophisticated machine learning unveil ferroptosis-driven regulatory pathways and immunotherapy potential in breast carcinoma
Enthalten in Discover oncology Bd. 16, 4.5.2025, Nr. 1, date:12.2025: 1-21
Online Ressource
Artikel 16080 Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning
Enthalten in Scientific reports Bd. 15, 30.4.2025, Nr. 1, date:12.2025: 1-11
Online Ressource


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