Input:
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Place your mtx and seq.dat.ss file inside the data folder.
An example of both files can be found inside the data folder.

Run:
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Run the pps.py file.
You can pass the name of the output file if you want as an argument. By default, its output.csv

Output:
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All output is written in the pps.csv file inside the output folder. Position count starts from 0.

Requirements:
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1. numpy
2. sklearn
4. lightgbm


Please cite:
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Shyantani Maiti, Atif Hassan, Pralay Mitra (In press). Boosting phosphorylation site prediction with sequence feature-based Machine learning. PROTEINS: Structure, Function, and Bioinformatics   
