Synergy Prediction Models

The IMPROVE project has curated a number of Synergy models. Model curation involves selecting a subset of community models and then modifying their software scripts to conform to a unified code structure.

The primary focus is on pan-cancer pan-drug models. Descriptions of the curated models and links to the code can be found below.

Model Selection

Qualitative selection criteria:

  • Models providing open-source code with: 1) comprehensive installation instructions of the computational environment, 2) data preprocessing scripts that take feature and response data and transform them into model input data, 3) scripts which execute model training.

  • Deep learning models implemented in TensorFlow/Keras or PyTorch.

  • Focus on pan-cancer multi-drug models utilizing cancer and drug representations as input features.

  • Preference for models utilizing conventional features.

  • End-to-end learning models, excluding those requiring feature pre-training or utilizing transfer learning.

  • Preference for recent peer-reviewed publications.

Hands-on selection criteria:

  • Successful installation of the computational environment.

  • Execution of preprocessing scripts to generating model-input data.

  • Reproducibility of key results as reported in the respective papers.

Model Standardization

Once the models met the selection criteria and were assessed for reproducibility, we proceeded with standardizing the model code structure. The standardization process is executed as follows:

  • Establishing reference prediction performance using the original model (e.g., r-square)

  • Restructuring the code into distinct preprocessing, training, and inference scripts following the IMPROVE guidelines

  • Verifying that the restructured code reproduces the reference performance

Further guidelines and details regarding code restructuring while leveraging the IMPROVE library are discussed here.

Curated Models

Currently, we have curated two synergy models. We forked the original repositories and conducted the model selection and standardization procedures as discussed above.