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 ...

Advances in high-throughput omic technologies allow for assaying a growing compendium of molecular layers, ranging from genome and epigenome profiling and transcriptomics to proteomics and ...

The following is a summary of a story that originally appears on the Trinity College of Arts & Sciences website. As a new assistant professor in Duke’s Department of Statistical Science, Lasse ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on their development of a new statistical method that improves analysis in ...