Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Clinical trials are pivotal for developing new medical treatments, yet they typically pose
some risks such as patient mortality, adverse events, and enrollment failure that waste …

The importance of uncertainty quantification is increasingly recognized in the diverse field of
machine learning. Accurately assessing model prediction uncertainty can help provide …

Bringing a novel drug from the original idea to market typically requires more than ten years
and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved …

Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the
statistical power of the treatment (eg, a new drug) in curing a certain disease. Clinical trial …

Drug discovery and development is a highly complex and costly endeavor, typically
requiring over a decade and substantial financial investment to bring a new drug to market …

Drug repurposing offers a promising avenue for accelerating drug development by
identifying new therapeutic potentials of existing drugs. In this paper, we propose a multi …

The surge in high-throughput omics data has reshaped the landscape of biological
research, underlining the need for powerful, user-friendly data analysis and interpretation …

Gibbs sampling is one of the most commonly used Markov Chain Monte Carlo (MCMC)
algorithms due to its simplicity and efficiency. It cycles through the latent variables, sampling …

Digital twin technology, a cutting-edge approach that creates dynamic digital replicas of
physical systems, is increasingly integral to various industrial applications. However, the …