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Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.
In Briefings in bioinformatics
Zhu Yi-Heng, Hu Jun, Ge Fang, Li Fuyi, Song Jiangning, Zhang Yang, Yu Dong-Jun
bioinformatics, deep-cascade forest, predictor, protein crystallization propensity, sequence-based feature
A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.
In Diagnostic and interventional radiology (Ankara, Turkey)
Ito Rintaro, Iwano Shingo, Naganawa Shinji
In Respirology (Carlton, Vic.)
Lyons M Melanie, Bhatt Nitin Y, Pack Allan I, Magalang Ulysses J
economics, global burden, obesity, obstructive sleep apnoea, risk factors
In Human brain mapping
Arabi Hossein, Bortolin Karin, Ginovart Nathalie, Garibotto Valentina, Zaidi Habib
PET, attenuation correction, deep learning, neuroimaging tracers, quantification
In Journal of neurology
Malik Preeti, Anwar Arsalan, Patel Ruti, Patel Urvish
Acute ischemic stroke, Artificial intelligence and stem cell therapy, DAWN, DEFUSE 3, Endovascular therapy, Large vessel occlusion, Telestroke
Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.
In Current rheumatology reports ; h5-index 35.0
PURPOSE OF REVIEW :
RECENT FINDINGS :
Bonakdari Hossein, Pelletier Jean-Pierre, Martel-Pelletier Johanne
Arthritis, Clinical decision-making, Data-driven, Interrupted time series, Management/treatment strategies, Time series