RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance

We introduce a Reference Drug-based Neural Detwork model (RefDNN) for predicting anticancer drug resistance and identifying biomarkers contributing drug resistance.

RefDNN can predict accurately whether a cell line is resistant to a new drug on which the model was not trained.

RefDNN requires gene expression profiles and drug molecular fingerprint data.

For more detail, please refer to Choi, Jonghwan, et al. "RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance." Scientific Reports 10 (2020):1-11

• Latest update: 06 February 2020

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SYSTEM REQUIERMENTS:

- RefDNN requires system memory larger than 24GB.

- If you want to use tensorflow-gpu, GPU memory of more than 4GB is required.

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PYTHON LIBRARY REQUIERMENTS:

- If you want to use only tensorflow-cpu:

    $ pip install -r requirements_cpu.txt
    
- If you want to use tensorflow-gpu:

    $ pip install -r requirements_gpu.txt

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HELP MESSAGES:

- The option parameter '-h' shows help message.

    $ python 1_nested_cv_baysian_search.py -h

    usage: 1_nested_cv_baysian_search.py [-h] [-o outputdir] [-b batchsize]
                                         [-t numtrainingsteps]
                                         [-s numbayesiansearch] [-k outerkfold]
                                         [-l innerkfold] [-v verbose]
                                         responseFile expressionFile
                                         fingerprintFile

    positional arguments:
      responseFile          A filepath of drug response data for TRAINING
      expressionFile        A filepath of gene expression data for TRAINING
      fingerprintFile       A filepath of fingerprint data for TRAINING

    optional arguments:
      -h, --help            show this help message and exit
      -o outputdir          A directory path for saving outputs
                            (default:'output_1')
      -b batchsize          A size of batch on training process. The small size is
                            recommended if an available size of RAM is small
                            (default: 64)
      -t numtrainingsteps   Number of training steps on training process. It is
                            recommended that the steps is larger than (numpairs /
                            batchsize) (default: 5000)
      -s numbayesiansearch  Number of bayesian search for hyperparameter tuning
                            (default: 20)
      -k outerkfold         K for outer k-fold cross validation (default: 5)
      -l innerkfold         L for inner l-fold cross validation (default: 3)
      -v verbose            0:No logging, 1:Basic logging to check process, 2:Full
                            logging for debugging (default:1)

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EXAMPLE:

- This example shows how to run the nested cross validation on the CCLE dataset.

- Since the default argument values of source code produce very very long process time(about 24 hours), we modify some values.

    $ python 1_nested_cv_baysian_search.py data/response_CCLE.csv data/expression_CCLE.csv data/fingerprint_CCLE.csv -o output_1_CCLE -s 10 -t 1000 -b 32

- The example requires about 30 minutes if GPU is exploited.

- This example will make a directory named 'output_1_CCLE' and save a result file named 'metrics_hyperparameters.csv' in the directory.

- The result file contains 3 metrics and hyperparameter values computed by the nested cross validation.

- When running the example, logging information like below is found.

    [START]
    [DATA] NUM_PAIRS: 5724
    [DATA] NUM_DRUGS: 12
    [DATA] NUM_CELLS: 491
    [DATA] NUM_GENES: 18926
    [DATA] NUM_SENSITIVITY: 2322
    [DATA] NUM_RESISTANCE: 3402
    [TIME] [1] 2019-4-12 15:16:18
    [TIME] [2] 2019-4-12 15:16:18
    [OUTER] [1/5] NOW TUNING THE MODEL USING BAYESIAN OPTIMIZATION...
    [OUTER] [1/5] BEST_HIDDEN_UNITS : 6
    [OUTER] [1/5] BEST_LEARNING_RATE_FTRL : 1.000e-01
    [OUTER] [1/5] BEST_LEARNING_RATE_ADAM : 6.827e-02
    [OUTER] [1/5] BEST_L1_REGULARIZATION_STRENGTH : 7.324e+00
    [OUTER] [1/5] BEST_L2_REGULARIZATION_STRENGTH : 1.052e-01
    [OUTER] [1/5] BEST_TRAINING_ACCURACY : -0.908
    [OUTER] [1/5] NOW TRAINING THE MODEL WITH BEST PARAMETERS...
    [OUTER] [1/5] BEST_TEST_ACCURACY : 0.907
    [OUTER] [1/5] BEST_TEST_AUCROC : 0.926
    [OUTER] [1/5] BEST_TEST_AUCPR : 0.940
    [TIME] [3] 2019-4-12 15:22:11
    [OUTER] [2/5] NOW TUNING THE MODEL USING BAYESIAN OPTIMIZATION...
    
    (...intermediate omission...)
    
    [OUTER] [5/5] NOW TRAINING THE MODEL WITH BEST PARAMETERS...
    [OUTER] [5/5] BEST_TEST_ACCURACY : 0.898
    [OUTER] [5/5] BEST_TEST_AUCROC : 0.944
    [OUTER] [5/5] BEST_TEST_AUCPR : 0.951
    [TIME] [3] 2019-4-12 15:45:36
    [TIME] [4] 2019-4-12 15:45:36
    [FINISH]

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