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Genialis ResponderID™ is a framework for biomarker discovery by modeling complex disease biology. ResponderID marries a novel “people first” approach to biomarker conceptualization with proprietary technology, validated signatures, and human data. The result is biomarkers that work in real patient populations and scale across clinical settings.

cancer patient

ResponderID includes algorithms that predict response to therapies targeting the tumor microenvironment, classify MSI, TMB, and other state-of-the-art immune signatures, and support the development of emerging drugs like KRAS inhibitors.

ResponderID is uniquely capable of developing biomarkers for drugs by modeling disease biology rather than relying on treatment response. Adhering to the FDA’s Good Machine Learning Practice guidelines and numerous international standards, our biomarkers are built to work for patients. These “just in time” biomarkers are especially well suited for helping new therapies reach the right patients who need them most.

Putting people first, Genialis provides a comprehensive picture of each patient’s disease. Genialis biomarkers not only stratify which patients are likely to respond to a particular drug but illuminate why a patient may or may not respond.

Cancer patient and complex biology
Cancer patient and complex biology
Cancer patient and complex biology
Cancer patient and complex biology

"We are so focused on getting the science right, because when we do, it changes lives."

Miha Štajdohar, PhD

CTO and Co-founder of Genialis

Key Advantages

Why is ResponderID better than other biomarker approaches?

  • We model biology, not clinical outcomes. This allows us to

  • Start the development early, even before the first clinical trial results are collected.
  • Solve the problem of small clinical datasets with other retrospective and proxy data.
  • Create models that are useful for different types of drugs and different cancer indications.
  • We cover the entire lifecycle of a biomarker, including

  • Hypotheses testing and data acquisition work together for rapid discovery and development.
  • Performance is evaluated on retrospective, prospective, and proxy data
  • Superb software engineering reliably clears regulatory hurdles
  • This allows us to develop a new generation of biomarkers that are

  • RNA-based, thus extremely information rich
  • Representative, with dozens to hundreds of genes to more fully capture complex biology
  • Robust, such thatAI captures redundant and non-linear relationships between genes

Find out how ResponderID has helped pharma companies develop more effective drugs with higher probability of clinical success, and diagnostics companies to deploy tests that lead to better treatment decisions:

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Proprietary technologies

bioinformatics software, machine learning sandbox

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Validated algorithms

published signatures & standard of care biomarkers

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Harmonized data assets

machine learning-ready ‘omics & clinical metadata

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Expert services

data analytics, strategic planning, program management

Contact us to learn more about how ResponderID can build biomarkers for better treatment options