Solutions

Data annotation for life sciences

Xana-Bio offers life sciences innovators fast and comprehensive medical data annotations, empowering them to accelerate drug discovery and harness real-world evidence at an unprecedented scale.

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Use Cases

Accelerating drug discovery and development with AI-enabled research and opportunity identification for approved therapies.

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Drug Discovery

AI is increasingly used in drug discovery to understand disease mechanisms, and predict response rates.

  • tick Image analysis
  • tick Multi-omics and clinical notes
  • tick Intellectual property knowledge graph
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Clinical development

From patient recruitment, to insights on current and past clinical trials, AI offers an opportunity to optimize trial design and success.

  • tick Predicting patient qualification
  • tick Real world data analysis
  • tick Reanalysis of failed clinical trials
  • tick Patient subgroup identification
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Commercial insights

Big data and AI are being leveraged to better understand competitors, as well as HCP behaviors.

  • tick Improve HCP targeting
  • tick Competitive analysis
  • tick Patient identification
  • tick Predicting adherence

Why Xana?

XANA accelerates AI development with speedy access to millions of diverse data points. Rooted in quality, it ensures consistent performance, advanced insights, and top-notch privacy across various industries and data types.

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Scale data annotation exponentially

Stop annotating data slowly with in-house teams or outsourcing to teams whose quality you can't control.
Access the Centaur Labs network of thousands of medical doctors, professionals, researchers and students for skilled annotation

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Take control of annotation quality

You define quality. We rigorously measure and manage it.
Incentivize labeler performance with small batch, mobile-first competitions. Labelers are only paid for performance and give 100% of their effort on every case

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Master your dataset

Proactively understand the nuances of your dataset, so your data works with you, not against you.
Leverage case-level insights to inform model development e.g. precision-recall curves, label distribution, labeler agreement

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