BeatAML CTD^2 DREAM Challenge: Example 2

Example implementation of a solution to subchallenge 2 of the BeatAML CTD^2 DREAM challenge. This example uses all input data types to train a Cox Model with ElasticNet regularization [1] to predict per-specimen hazard.

To train a model

• Run Jupyter with docker run -p 8888:8888 -v "$PWD:/home/jovyan" jupyter/scipy-notebook
• Stdout will include a URL to open the notebook
• Go through the steps in index.ipynb
• The model will be stored in model/ in a bunch of files
• Read more about the model below

To Run Your Model on Training Data

This model can be run on the same data it was trained on, to test whether the Dockerfile works:

SYNAPSE_PROJECT_ID=<...>
docker build -t docker.synapse.org/$SYNAPSE_PROJECT_ID/sc2_model .
docker run -v "$PWD/training/:/input/" -v "$PWD/output:/output/" docker.synapse.org/$SYNAPSE_PROJECT_ID/sc2_model 

Submitting to Synapse DockerHub

SYNAPSE_PROJECT_ID=<...>
docker login docker.synapse.org
docker build -t docker.synapse.org/$SYNAPSE_PROJECT_ID/sc2_model .
docker push docker.synapse.org/$SYNAPSE_PROJECT_ID/sc2_model

The Model

See index.ipynb for more explanation of the feature selection.

[1] Powered by scikit-survival.