Results 191 to 200 of about 33,486 (254)
Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm. [PDF]
Liang Y +5 more
europepmc +1 more source
An overview of grain boundary engineering in the field of electrocatalysis. ABSTRACT Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs)
Jingyu Gao +8 more
wiley +1 more source
Predicting anti-cancer drug sensitivity through WRE-XGBoost algorithm with weighted feature selection. [PDF]
Jiang Y, Chen M, Xiong Z, Qin Y.
europepmc +1 more source
‘biomod2' – extending presence–absence species distribution models to multiple data types
The R package ‘biomod2' is one of the most widely used and versatile tools for species distribution modelling (SDM), enabling ecologists to calibrate, evaluate, and project species–environment relationships across space and time using multiple modelling algorithms and ensemble forecasting.
Maya Guéguen +3 more
wiley +1 more source
This work systematically reviews the key factors influencing the performance of low‐temperature NH3‐SCR. The mechanism and challenges of defect engineering strategies, such as oxygen vacancies, heteroatom doping, crystal facet exposure, and surface reconstruction, in controlling both activity and selectivity were analyzed.
Rongrong Kan +3 more
wiley +1 more source
Prediction of LVEF improvement in patients with HFrEF and HFmrEF following treatment with Sacubitril/Valsartan. Workflow of this study investigating the functional capacity improvement in response to Sacubitrail/Valsartsan in a real‐world scenario of heart failure treatment.
Florian Appenzeller +8 more
wiley +1 more source
Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard. [PDF]
Bukowski M +3 more
europepmc +1 more source
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu +7 more
wiley +1 more source
Abstract Objective Epilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a commonly prescribed first‐line ASM, yet only approximately half of patients achieve sustained seizure freedom. Treatment selection remains largely empirical.
Simeon Platte +15 more
wiley +1 more source
An algorithm for seizure detection in rodents
Abstract Objective Epilepsy animal research often relies on long‐term intracranial electroencephalographic (iEEG) recordings. Here, we describe an artificial neural network (ANN) algorithm for automatic detection of seizures. Methods The algorithm was trained on iEEG recordings of three mouse models of chronic epilepsy: (1) the pilocarpine model of ...
Lyna Kamintsky +9 more
wiley +1 more source

