Proceedings of the National Academy of Sciences of the United States of America
Jun 29, 2021
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational dat...
Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas,...
Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in...
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing u...
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances ...
The Journal of bone and joint surgery. American volume
Jun 16, 2021
BACKGROUND: Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning ...
The Journal of clinical endocrinology and metabolism
Jun 16, 2021
CONTEXT: Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly.
The Journal of clinical endocrinology and metabolism
Jun 16, 2021
CONTEXT: Prediction of AH is frequently undertaken in the clinical setting. The commonly used methods are based on the assessment of skeletal maturation. Predictive algorithms generated by machine learning, which can already automatically drive cars ...
MOTIVATION: Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while t...
As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes-from protein folding to viral cell entry-yet are still ...
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