使用wiZAN和其他算法预测化学蛋白质关联附matlab代码.zip

# PyDTI -- a Python library for drug-target interaction prediction version 1.0, June 08 2015 -------- This package is written by: Yong Liu Email: liuy0054@e.ntu.edu.sg 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. -------- 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 -------- 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/". -------- 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