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

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

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 ...

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 ...

Test equating constitutes a critical element of modern assessment practices, ensuring that scores from different test forms are directly comparable. Recent advancements have pooled statistical ...

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 ...

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), ...

Misuse of statistics in medical and sports science research is common and may lead to detrimental consequences to healthcare. Many authors, editors and peer reviewers of medical papers will not have ...

This workshop will explore opportunities to use statistical and data-driven methods for additive manufacturing qualification, including approaches that enhance dimensional accuracy and recent advances ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches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