Building a Solid Data Foundation for Cutting Edge ML

Key Highlights

To effectively train predictive models and drug discovery, we need large volumes of high-quality, clean, and linked data. However, preparing these data can be a costly and time-consuming task. In this webinar, Mya Steadman elucidates on the theme- “Building a Solid Data Foundation for Cutting Edge ML”  and discusses strategies for training and enhancing the accuracy of predictive models.

Key Points Addressed

  • Improving the accuracy of the AI/ML model with clean, harmonized, and structured data.
  • Harmonized metadata leads to improved performance and reduced time for actionable insights.
  • Elucidata collaborated with an early-stage therapeutics company studying AML to identify novel targets along with patient stratification.
  • This collaboration led to the identification and validation of novel targets at a rate four times faster than the traditional approach.

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