|
11001 |
Predicting the stereoselectivity of chemical reactions by composite machine learning method
Enthalten in Scientific reports Bd. 14, 27.5.2024, Nr. 1, date:12.2024: 1-12
|
|
|
|
11002 |
Predicting the success of startups using a machine learning approach
Enthalten in Journal of innovation and entrepreneurship Bd. 13, 28.10.2024, Nr. 1, date:12.2024: 1-27
|
|
|
|
11003 |
Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP
Enthalten in BMC genomics Bd. 23, 7.1.2022, Nr. 1, date:12.2022: 1-18
|
|
|
|
11004 |
Predicting the time to get back to work using statistical models and machine learning approaches
Enthalten in BMC medical research methodology Bd. 24, 29.11.2024, Nr. 1, date:12.2024: 1-8
|
|
|
|
11005 |
Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
Enthalten in Scientific reports Bd. 15, 8.3.2025, Nr. 1, date:12.2025: 1-22
|
|
|
|
11006 |
Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration
Enthalten in Nature Communications Bd. 16, 2.1.2025, Nr. 1, date:12.2025: 1-15
|
|
|
|
11007 |
Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms
Enthalten in Scientific reports Bd. 13, 30.9.2023, Nr. 1, date:12.2023: 1-13
|
|
|
|
11008 |
Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors
Enthalten in BMC health services research Bd. 25, 12.3.2025, Nr. 1, date:12.2025: 1-27
|
|
|
|
11009 |
PREDICTING TRAFFIC CONGESTION DURING COVID19 USING HUMAN MOBILITY AND STREET-WASTE FEATURES
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-4/W3-2022, 2022: 301-308. 8 S.
|
|
|
|
11010 |
Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models
Enthalten in Pain and therapy Bd. 14, 25.12.2024, Nr. 1, date:2.2025: 359-373
|
|
Administration
