A statistical machine learning toolbox for estimating models, distributions, and functions with context-specific parameters with applications to biological and medical systems.
Built using Contextualized.ML
BioContextualized contains:
| sc-contextualized.ml | Contextualized tools for analyzing single-cell omics data. |
| MedContextualized | Contextualized tools for analzying medical data. |
pip install git+https://github.com/blengerich/BioContextualized.git
- Automated Interpretable Discovery of Heterogeneous Treatment Effectiveness: A COVID-19 Case Study
- NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
- Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning
- Personalized Survival Prediction with Contextual Explanation Networks
Please get in touch with any questions, feature requests, or applications.