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Bayesian Scientific Machine Learning (SciML) from PK to Synthetic Controls and Digital Twins

Make better decisions by using disease-specific models

How predictive is my new biomarker? What attributes differentiate responders from non-responders? How fast will a patient progress on or off treatment? What is the probability that my new treatment is better than the competitor or standard of care? What is the best dose for a specific treatment or a specific person? These are some of the questions we are working on with leading Pharma and Biotech companies.



Our models are generative, transparent, explainable, and testable. In contrast to black box methods, we know what these models are doing.


Predictive Accuracy

In-sample predictions are easy. Our models make well calibrated predictions out of sample: for a new patient, a new study arm, and even a new trial.

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Recent advances in computational statistics allow us to tackle models previously thought too difficult due to non-linearities and the number of unknowns.


Small Data and RWE

Our models work well in the small data regime, such as in platform trials in Oncology and Rare Diseases, where we need to take advantage of information external to the clinical trial.

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Joint Outcome-Biomarker Models

Linking the model for the hazard with     

sub-models for individual biomarkers.   

Joint models for comorbidities.

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Complex PK/PD Models

Using Ordinary Differential Equation (ODE) solvers we encode how the drug moves through different parts of the body.


Biomarker Screening Models

Retain proper uncertainties and produce more accurate predictions than popular tools like Lasso and PCA.

Want to learn more?

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