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Found 4 papers, 4 papers with code
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19.
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights.
Scaling structural learning with NO-BEARS to infer causal transcriptome networks
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data.
Enhancing high-content imaging for studying microtubule networks at large-scale
Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed.
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