DeepTTC: a transformer-based model for predicting cancer drug response
Identification of new lead molecules for treatment of cancer is often the result of more than a decade of dedicated efforts. Before these painstakingly selected drug candidates are used in clinic , their anti-cancer activity was generally varified by vitro cell experiments. Therefore, accurately prediction cancer drug response is the key and challenging task in anti-cancer drugs design and precision medicine.
DeepTTC is an end-to-end deep learning model to predict the anti-cancer drug response. DeepTTC takes gene expression data and chemical strcutures of drug for drug response prediction. Specifically, get inspiration from natural language processing, DeepTTC use transformer for drug representation learning.
- This model is curated as a part of the IMPROVE project.
- Original code: GitHub
- Original paper: DeepTTA: a transformer-based model for predicting cancer drug response
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Build Singularity image
./build.sh DeepTTC.build -
Run Singularity image
./run.sh writable/DeepTTC_YYYY_MM_DD -
Navigate to DeepTTC folder
cd $PATH_TO_WRITABLE$/DeepTTC_YYYY_MM_DD/DeepTTC/
python3 DeepTTC_candle.py --config_file DeepTTC.build
mode - Execution mode. Available modes are: 'run', 'benchmark'
generate_input_data - 'True' for generating input data anew, 'False' for using stored data
benchmark_result_dir - Directory for benchmark output
use_lincs - Whether to use a LINCS subset of genes ONLY
cancer_id - Column name for cancer
drug_id - Column name for drug
target_id - Column name for target
sample_id - Column name for samples/cell lines
train_data_drug - Drug data for training
test_data_drug - Drug data for testing
train_data_rna - RNA data for training
test_data_rna - RNA data for testing
benchmark_dir - Directory with the input data for benchmarking
output_dir - Output directory for saving model
model_name - File name for the model
rng_seed - Seed for the random number generator
vocab_dir - Directory with ESPF vocabulary
input_dim_drug - Input size of the drug transformer
transformer_emb_size_drug - Size of the drug embeddings
dropout - Dropout rate for classifiers
transformer_n_layer_drug - Number of layers for drug transformer
transformer_intermediate_size_drug - Size of the intermediate drug layers
transformer_num_attention_heads_drug - number of attention heads for drug transformer
transformer_attention_probs_dropout - Dropout rate for the attention layers
transformer_hidden_dropout_rate - Dropout rate for the transformer networks' hidden layers
learning_rate - Learning rate of the neural networks
batch_size - Size of the training batches
input_dim_drug_classifier - Input size of the drug classifier
input_dim_gene_classifier - Input size of the gene classifier