Reducing liver fat has become a central target in the development of therapies for metabolic dysfunction–associated ...
The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...
Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...
This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...
Objective: Based on the Bayesian network, this study investigates the impact pathways of multidimensional factors related to the living environment—specifically housing factors, exposure to daily ...
In the predawn hours of August 19, 2024, bolts of lightning began to fork through the purple-black clouds above the Mediterranean. From the rail of a 184-foot vessel, a 22-year-old named Matthew ...
Single-cell transcriptomic architecture for deciphering the complexity of tumor microenvironment in ampulla of Vater carcinoma. Advancing treatment outcomes for peritoneal surface malignancies in low- ...
The recently concluded MWC 2025 showcased how cutting-edge advancements in AI, digital architectures and mobile connectivity are accelerating digitalization, reinventing industries and transforming ...
Abstract: Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas ...
Abstract: This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to ...
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