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SciBite / Knowledge Hub / Resources / Using knowledge graphs to drive epigenetic target discovery in oncology with AstraZeneca [Use Case]

Using knowledge graphs to drive epigenetic target discovery in oncology with AstraZeneca [Use Case]

While the impact of GenAI on pharma R&D is promising, it’s crucial to prioritize actual outcomes over hype. That’s why the relevant, quality dataset, with enriched metadata can help turn content into insights.

So, what constitutes the right data foundation? Pharmaceutical companies like yours face a daunting challenge in staying on top of the published literature, as well as ensuring it’s in a format that can be easily integrated with internal data sources. To ensure data quality and provenance, it’s crucial to access the most relevant information. But, how do you know you have the right data you need?
  • Collecting information based on journal title or subject collection alone?
  • You could miss relevant articles and insights – up to 2.5x more – dependent on the therapeutic area.

Learn how by working with Elsevier an Oncology team at AstraZeneca was able to assemble the literature base and build a powerful suite of computational tools to increase the efficiency and depth of target discovery in the epigenetic space, including an order of magnitude increase in the search space for new epigenetic targets.

Want to learn more about how AI and the right data can make a difference to pharma?
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