Skip to content

Machine learning analysis for prediction of synergistic drug behavior

Notifications You must be signed in to change notification settings

joycex99/drug-synergy-modeling

Repository files navigation

Modeling Drug Synergy

This repository holds the code for a machine learning-based model for predicting synergistic drug behaviors on cancer cell lines. The ML is largely performed on top of the scikit-learn library, with additional work done in numpy/scipy/pandas. Each IPython Notebook holds a different version of the model and/or a major step in data manipulation or feature engineering. Research is still in process.

Several key components include:

  • Cross-validation of several different regressors and classifiers, including random forest, adaboost, gradient boosting, and svms.
  • Drug-drug mapping through shared targets
  • Construction of a PPI (protein-protein interaction) network and implementation of a path-searching algorithm to map target interactions to drug interactions
  • Implementation of a genetic algorithm to perform parameter tuning and feature selection

The model currently achieves ~0.66 average pearson correlation for regression, and a 0.73 average classification accuracy of synergistic vs. non-synergistic compound/compound/cell line combinations (0.76 AUC, 0.79 F1).

When tasked with identifying clinically significant cases of synergy (>30% change integrated over a log2 concentration space), it achieves a classification accuracy of 0.83 and an AUC of 0.79.

About

Machine learning analysis for prediction of synergistic drug behavior

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published