This package is written by:
Mohammad Amin Khodamoradi
Email: [email protected]
Department of Computer Science, Shahid Beheshti university, Tehran, Iran Joint Schoole of Bioinformatic, IPM, Tehran, Iran
For any questions regarding to this library, please feel free to contact the author.
PyDTI is implemented by Python 3.6.7, which can be downloaded from: https://www.python.org/downloads/release/python-367/. PyDTI requires several other Python packages, including Numpy, scikit-learn, Scipy, tensorflow == 1.7, edward, matplotlib, zmq, pyzmq and pymatbridge (a bridge between Python and Matlab).
The original packages can be found here: http://www.numpy.org/ http://scikit-learn.org/stable/ http://www.scipy.org/ http://arokem.github.io/python-matlab-bridge/ . . .
Note that zmq, pyzmq and pymatbridge are only required by matlab implementations. The 64-bit Windows binaries of Numpy, scikit-learn, and Scipy can also be found at: http://www.lfd.uci.edu/~gohlke/pythonlibs/.
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Add the folder "$PYTHON_ROOT$/Scipts/" to the system path. Please replace "$PYTHON_ROOT$" with the root folder of Python in your system.
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Install the packages using pip utility. Open a console and type the following to install
pip install numpy scipy scikit-learn
To get the results of different methods, please run PyDTI.py by setting suitable values for the following parameters:
--method set DTI prediction method
--dataset: choose the benchmark dataset, i.e., nr, gpcr, ic, e
--folder: set the the folder that contains the datasets (default "datasets/")
--csv: choose the cross-validation setting, 1 for CVS1, 2 for CVS2, and 3 for CVS3, (default 1)
--specify-arg: 0 for choosing optimal arguments, 1 for using default/specified arguments (default 1)
--method-opt: set arguments for each method (method ARGUMENTS have the form name=value)
--predict-num: 0 for not predicting novel DTIs, a positive integer for predicting top-N novel DTIs (default 0)
Here are some examples:
(1) run a method with default arguments
python PyDTI.py --method="nrlmf" --dataset="nr"
python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=2
python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=2 --specify-arg=1
(2) run a method with specified arguments
python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=1 --method-opt="r=100"
python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=1 --method-opt="c=5 K1=5 K2=5 r=100 lambda_d=0.125 lambda_t=0.125 alpha=0.25 beta=0.125 theta=0.5"
You can refer to lines 47-58 in the PyDTI.py for the default parameters of each DTI prediction method.
(3) choose the optimal parameters for a method
python PyDTI.py --method="nrlmf" --dataset="nr" --cvs=1 --specify-arg=0
(4) predict the top-100 novel DTIs
python PyDTI.py --method="nrlmf" --dataset="nr" --predict-num=100 --method-opt="r=100"
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You can run sat_analysis.py for the statistical comparision between NRLMF and other baseline methods on all datasets, under different cross-validation settings. Note that you should first obtain the auc and aupr results of each method.
python sta_analysis.py
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