Comorbidity data can reveal unexpected and insightful connections between diseases. Understanding shared risks and underlying mechanisms are critical for informing patient care, yet these analyses often present significant challenges for researchers.

Using a case study on the correlation between asthma and inflammatory bowel disease (IBD) in women, this webinar will demonstrate how comorbidity data can be integrated with harmonized omics datasets and knowledge graph–based analytical approaches.

Discover how you can combine large-scale, preprocessed omics resources with network-driven analytics to uncover shared molecular mechanisms across diseases. Leveraging extensive collections of preprocessed data, the approach identifies gender-specific differentially expressed genes within each disease condition. Findings are then contextualized within a molecular interaction network to clarify associated biological functions and support hypothesis generation.

This webinar is based on a 2022 NHS population study on multimorbidity and comorbidity1, which demonstrates how large-scale datasets can be used to propose mechanistic hypotheses that help explain observed patterns in patient health.

Reference:

[1] Kuan, Valerie, et al. “Identifying and visualizing multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study.” The Lancet Digital Health 5.1 (2023): e16-e27.