BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not a...
Journal of chemical information and modeling
Nov 8, 2020
This work considers strategies to develop accurate and reliable graph neural networks (GNNs) for molecular property predictions. Prediction performance of GNNs is highly sensitive to the change in various parameters due to the inherent challenges in ...
In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the ...
OBJECTIVES: This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews.
International journal of pharmaceutics
Nov 7, 2020
The ability to predict mechanical properties of compacted powder blends of Active Pharmaceutical Ingredients (API) and excipients solely from component properties can reduce the amount of 'trial-and-error' involved in formulation design. Machine Lear...
BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversit...
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy o...
BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the cur...
This study applied machine learning regression to predict motor function after stroke based on multimodal magnetic resonance imaging. Fifty-four stroke patients, who underwent T1 weighted, diffusion tensor, and resting state functional magnetic reson...
Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven c...
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