This project addresses the challenge of predicting drug and drug combination response in cancer using a deep learning model.
This can personalize treatment plans and improve our understanding of drug mechanisms.
We utilized various data types, including drug response, cancer type, mutation data, and RNA expression levels. We focused on building interpretable models, employing techniques like Shapley values to identify key features influencing drug response predictions.
- Coding scripts and saved models for the deep learning model.
- Model descriptions and test data predictions.
- Short answer responses on model training and interpretation.
Link: data/Hackathon Challenge #2 Drug Combination Modeling.pdf
Submission from Harikrishna Dev and Shashank Ravishankar.