Webinar

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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Building a Solid Data Foundation for Cutting Edge ML

What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

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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Real-World Applications We’ll Cover

Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.

Why This Matters for Biomedical Researchers

If you’re working with complex biological data, you may be asking:

Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?

Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.

Meet the Experts of this discussion

Key Takeaways

How data providers ensure adherence to quality standards through validation and compliance.

How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.

Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.

Key Takeaways

Reduce operational costs by streamlining data delivery through reusable, governed products.

Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.

Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.

Equip cross-functional teams to act on trusted data—faster, and with greater confidence.

Who Should Attend?

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Other Resources

All Webinars Case Studies Dataset Roundup Documentation Glossary Solution Briefs Whitepapers