Integrating Mendelian randomization and literature mining to map breast cancer risk factors
Breast cancer research spans epidemiology, molecular biology, clinical trials, and a vast and rapidly growing literature. One challenge is triangulating across these evidence types: when different sources point in the same direction, we can be more confident we are seeing something causal rather than correlational.
In a paper led by Marina Vabistsevits published in the Journal of Biomedical Informatics, we show how to bring two complementary sources together:
- Mendelian randomization (MR) evidence generated at scale using MR-EvE (“Everything-vs-Everything”), and
- Literature-mined relationships stored in EpiGraphDB, our biomedical knowledge graph.
