The combination drug response predictor (Combo) shows how to predict tumor cell line growth to drug pairs in the NCI-ALMANAC database using artificial neural networks.
Data scientists interested in bioinformatics; computational cancer biology, drug discovery, and machine learning.
Data scientists can train the provided untrained model with the shared preprocessed data or with their own preprocessed data, or can use the trained model to predict the drug response from the NCI-ALMANAC study. The provided scripts use data that have been downloaded from NCI-ALMANAC and normalized.
Data scientists can use multiple machine learning techniques to predict drug response. The general rule is that classical methods like random forests would perform better for small datasets, while neural network approaches like Combo would perform better for relatively larger datasets.
The following components are in the Model and Data Clearinghouse (MoDaC):
- The Pilot 1 Cancer Drug Response Prediction Dataset asset contains processed training and test data.
- The Combination Drug Response Predictor (Combo) asset contains the untrained model topology, and the trained model weights to be used in inference.
Xia, Fangfang, et al. "Predicting tumor cell line response to drug pairs with deep learning." BMC bioinformatics 19.18 (2018): 71-79.
Refer to this README.