This playbook explores how researchers can generate–and integrate–different dimensions of multimodal data in their drug discovery programs.
As biomarker discovery evolves, researchers are seeking more detail and deeper insights. That’s why more people are embracing multimodal data, where genomics and transcriptomics join proteomics, metabolomics and other data types for a comprehensive and composite biological view.
But multimodal data—which comes from dispersed sources—isn’t always ready to use. It has to be cleaned, organized and integrated with other datasets correctly, and that’s the challenge many life sciences organizations are facing. However, with advanced tools, drug discovery researchers can apply multimodal data science in completely new ways. In this playbook, you’ll learn:
- The context and forces shaping researchers’ interest in multimodal data
- Opportunities to overcome integration challenges in multimodal data
- Why curated, high-quality data matters—and where to find it
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