"Team Builds Modeling Systems Identifying Gene-Drug And Environment Interaction"
A team of researchers at the Icahn School of Medicine at Mount Sinai and the University of Washington has designed a modeling system that integrates genomic and temporal information to infer causal relationships between genes, drugs, and their environment, allowing for a more accurate prediction of their interactions over time. The work is described in a paper published in September in Nature Communications. "Understanding how a person's environment, diet, medications, and other factors impact disease-associated traits over time has the potential to more accurately model an individual's risk of disease,” said Eric Schadt, PhD, dean for precision medicine at the Icahn School of Medicine at Mount Sinai and CEO of Sema4, and a co-author of the paper. "Our new tools offer a fundamental step forward by analyzing genomic data over time. This type of approach will be particularly useful for medical research on aging and ultimately could enhance our ability to predict disease risk, making earlier interventions possible to treat or prevent disease altogether,” said senior author, Jun Zhu, PhD, professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai and head of data science at Sema4.
- Eric Schadt, PhD, Dean, Precision Medicine, Icahn School of Medicine at Mount Sinai, CEO, Sema4
- Jun Zhu, PhD, Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, Head, Data Sciences, Sema4
- Adam Margolin, PhD, Chair, Department of Genetics and Genomic Sciences, Senior Associate Dean, Precision Medicine, Icahn School of Medicine at Mount Sinai