|
26631 |
The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes
Enthalten in Scientific reports Bd. 13, 19.1.2023, Nr. 1, date:12.2023: 1-14
|
|
|
|
26632 |
The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium
Enthalten in Molecular psychiatry Bd. 28, 2.5.2023, Nr. 10, date:10.2023: 4307-4319
|
|
|
|
26633 |
The future of machine learning for small-molecule drug discovery will be driven by data
Enthalten in Nature computational science Bd. 4, 15.10.2024, Nr. 10, date:10.2024: 735-743
|
|
|
|
26634 |
The Game of Drones: rapid agent-based machine-learning models for multi-UAV path planning
Enthalten in Computational mechanics Bd. 65, 16.9.2019, Nr. 1, date:1.2020: 217-228
|
|
|
|
26635 |
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
Enthalten in Communications chemistry Bd. 7, 12.6.2024, Nr. 1, date:12.2024: 1-11
|
|
|
|
26636 |
The greedy side of the LASSO: new algorithms for weighted sparse recovery via loss function-based orthogonal matching pursuit
Enthalten in Sampling theory, signal processing, and data analysis Bd. 23, 10.2.2025, Nr. 1, date:6.2025: 1-40
|
|
|
|
26637 |
The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches
Enthalten in Discover sustainability Bd. 6, 7.2.2025, Nr. 1, date:12.2025: 1-24
|
|
|
|
26638 |
The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach
Enthalten in Neurological sciences Bd. 41, 28.10.2019, Nr. 2, date:2.2020: 459-462
|
|
|
|
26639 |
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation
Enthalten in Implementation science Bd. 12, 18.10.2017, Nr. 1, date:12.2017: 1-12
|
|
|
|
26640 |
The identification of $$\varvec\alpha }}}$$ α -clustered doorway states in $$^44,48,52}$$ 44 , 48 , 52 Ti using machine learning
Enthalten in The European physical journal / A / Hadrons and nuclei Bd. 57, 29.3.2021, Nr. 3, date:3.2021: 1-12
|
|
Administration
