Often scientists may not know the exact biological mechanisms dictating the data signatures. For example, which risk factors drive responders to a cancer treatment or how a biomarker is related to clinical outcomes. Current data science methods require big data, and they ignore prior knowledge of the problem at hand. Pumas-AI is poised to disrupt this. DeepPumas enables seamless integration of domain-specific knowledge and data-science methodology, reducing dependence on data size and enabling faster decision-making.
Here, we will learn, hands-on, how DeepPumas can automatically discover complex predictive factors to individualize predictions. Furthermore, we will learn how dynamical systems that model the longitudinal evolution of patient outcomes can be augmented by machine learning – enabling data-driven discovery of the underlying biology. Together, this enables effective use of data to rapidly develop models that predict individual outcomes from heterogeneous sources of patient data.
Applicable across the whole chain of drug development, from lead generation, quality by design manufacturing, clinical research, and market research to individualized patient management, DeepPumas is not an incremental improvement but a game-changer.
The workshop is split in two, where the first day is dedicated to learning the powerful Pumas software for pharmacometric modeling. During the second day, we learn about machine learning and how it can be seamlessly embedded in pharmacometric models using DeepPumas.
We use Material for MkDocs (MIT License) as the static site generator.
- Clone the repository
- Install Material for MkDocs with
pip install mkdocs-material - Create or review content
- Preview the site with
mkdocs serve - Make a Pull Request
- Niklas Korsbo - niklas@pumas.ai
- Mohamed Tarek - mohamed@pumas.ai
- Jose Storopoli - jose@pumas.ai
This content is licensed under Creative Commons Attribution-ShareAlike 4.0 International.
