High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of ...

Examination of the conflicting statistical methods currently used in scientific inference reveals an increasing awareness of the utility of likelihood. The concept of prior likelihood is introduced as ...

Through tumor profiling and statistical analysis, precision oncology uses genomic and other biomarkers to drive discovery, drug development, and clinical care for patients with cancer. It intends to ...

Our laboratory has developed a range of data analysis workflows that incorporate advanced statistical and computational methods to interpret the complex molecular datasets generated by MS technologies ...

A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions -- such as cell types or tissues. A new statistical ...

Various statistical forecasting methods exist designed for use with slow-moving products, new product introductions, stable mature products and products with erratic ...

Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...

Finding a convincing explanation of a complex issue is no easy task. The decision about what qualifies as the "best" solution is inevitably subject to biases and approximations. Coupling statistics ...

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 ...

The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing—class time ...