Reduce Discovery Risk Using Comorbidity Data, Omics and Knowledge Graphs, Upcoming Webinar Hosted by Xtalks

In this free webinar, see how large-scale, harmonized omics data combined with knowledge graph analytics can uncover non-obvious diseases, disease connections and shared molecular mechanisms. The featured speaker will share practical insights into identifying gender-specific molecular signatures (e.g., in asthma-IBD comorbidity) to inform more precise hypothesis generation and target discovery. Attendees will explore a repeatable analytical framework for moving from population-level comorbidity patterns to mechanistic, testable biological hypotheses that can guide discovery research.

TORONTO, March 23, 2026 /PRNewswire/ -- 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.

The featured speaker will 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 will be 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.

Register for this webinar to learn how to integrate comorbidity data with omics and knowledge graphs to generate testable biological hypotheses.

Join Ruth Stoney, PhD, Senior Field Application Scientist, QIAGEN Digital Insights, for the live webinar on Friday, April 24, 2026, at 10am EDT (4pm CEST/EU-Central).

For more information, or to register for this event, visit Reduce Discovery Risk Using Comorbidity Data, Omics and Knowledge Graphs.

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.

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