yc930401 / PyDTI

PyDTI -- a Python library for drug-target interaction prediction

version 1.0, June 08 2015

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This package is written by:

Yong Liu

Email: [e-mail]

School of Computer Engineering, Nanyang Technological University, Singapore Joint NTU-UBC Research Centre of Excellence in Active Living for Elderly (LILY), Nanyang Technological University, Singapore

For any questions regarding to this library, please feel free to contact the author.

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PyDTI is implemented by Python 2.7.9, which can be downloaded from: https://www.python.org/downloads/release/python-279/. PyDTI requires several other Python packages, including Numpy, scikit-learn, Scipy, 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 pymatbridge is only required by KBMF2K. The 64-bit Windows binaries of Numpy, scikit-learn, and Scipy can also be found at: http://www.lfd.uci.edu/~gohlke/pythonlibs/.

1. Add the folder "$PYTHON_ROOT$/Scipts/" to the system path. Please replace "$PYTHON_ROOT$" with the root folder of Python in your system.

2. Install the packages using pip utility. Open a console and type the following to install

pip install numpy scipy scikit-learn

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For the KBMF2K method, please download the matlab code implemented by M. Gonen from http://users.ics.aalto.fi/gonen/bioinfo12.php and put the matlab code in the sub-folder "kbmf2k/". To connect the Matlab code and Python, we need to define a Matlab function:

function predictR = kbmf(args)
	Kx = args.Kx;
	Kz = args.Kz;
	Y = args.Y;
	R = args.R;
	state = kbmf_regression_train(Kx, Kz, Y, R);
	prediction = kbmf_regression_test(Kx, Kz, state);
	predictR = prediction.Y.mu;
end

Save this function into a Matlab file named kbmf.m and put this file into the subfolder "kbmf2k/".

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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"

4. 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