Researchers at Vanderbilt University Medical Center and the University of Cambridge have developed a method of or predicting gene expression in hard-to-access tissues like the brain from more accessible tissues, including whole blood.

A recent Vanderbilt study is one of the first to use statistical and population-based methods to understand comorbidities associated with developmental language disorder at a population level.

The expected course of a patient's cancer prognosis has traditionally been judged by its type, stage and microscopic aggressiveness, but patients with the same presentation can still have widely divergent outcomes. Researchers from Vanderbilt-Ingram Cancer Center have discovered that differences in tumor mutation burden are a major reason for this divergence.

Vanderbilt University Medical Center's Lea Davis, PhD, and longtime colleagues Ekaterina (Katya) Khramtsova, PhD, and Barbara Stranger, PhD, are among seven authors of a paper published May 11 in the journal Cell that outlines “best practices” for evaluating the impact of both sex and gender on human health.

A transcontinental research effort led by scientists from Vanderbilt University Medical Center and the University of Michigan has upended some long-standing assumptions about mutations — how often they occur, what causes them and what they do.

A machine learning method based on neural networks outperformed a mutational scanning model at identifying disease-causing mutations in an Alzheimer’s disease protein, suggesting the method could be useful for facilitating therapeutic design.

Polygenic risk scores — scores that reflect the influence of common genetic variants — could be used to predict the likelihood of developing chronic overlapping pain conditions and guide biomarker and targeted prevention efforts.