Models to Enable Computational Oncology

Our Model Types

Joint Survival Models

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Linking the model for the hazard with submodels for individual biomarkers. Joint models for comorbidities.

In Phase II and Phase III these models inform the risk of an event by incorporating individual patients’ biomarker evolution, which may be governed by a complex non-linear process.

Main Challenge

Non linear process dynamics, predicting for the study arm and for the individual patient.

Our Unique Solution

Continuing to incorporate mechanistic and semi-mechanistic models at this stage to help guide efficacy.

Pharmacometric Models

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

These models are often used in preclinical and Phase I trials in order to establish safe dosing regimens. While safety is the main concern, we strive to detect signs of early efficacy.

Main Challenge

Small data and non-linear system dynamics

Our Unique Solution

We are able to infer both the population and individual level parameters and present it to non-specialists in an easily interpretable form.

Genomics Models

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We don't rely on GWAS. We include genomic data into a more complex model of the disease to improve the precision of estimated clinical endpoints and to understand the heterogeneity of response.

This is the wide data of the big data spectrum. Traditional machine learning approaches including clustering and dimensionality reduction are unlikely to uncover meaningful clinical relationships.

Main Challenge

Too many possible combinations frequently guide the researchers to confuse signal with noise.

Our Unique Solution

By using better priors and including candidate features in our generative models allows us to test their efficacy and construct proper uncertainty intervals.