A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

A big part of his work is figuring out when a statistical method is truly the best choice. Some problems have straightforward ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...

To claim federal income tax credits for a research project, a taxpayer must prove that the project satisfies each prong of a four-part test. In IRS Field Attorney Advisory 20212501F (June 25, 2021), ...

Our lab has developed many data analysis workflows adapting and integrating sophisticated statistical methods to evaluate complex molecular datasets that we obtain with MS technologies. We are ...

Bench to bedside, the Center for Clinical and Translational Science (CCTS) seeks to improve well-being and health in communities across the Deep South through collaborative partnerships and scientific ...

Scientists from Peking University conducts a systematic review of studies on integrating machine learning into statistical methods in disease prediction models. Researchers from Peking University have ...

What are your research topics? I develop statistical methods that help people make sense of complex and high-dimensional data — for example, information collected from many sensors, locations, or ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches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 ...