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Protein subcellular localization prediction using graph convolutional network and protein embedding

Python 73.45% R 6.40% Shell 2.94% TeX 17.20%

plocnet's Introduction

pLocNet

Protein subcellular localization prediction using graph convolutional network and protein embedding

Directory structure

  • /preprocess contains scripts for preprocessing data.
  • /data contains preprocessed data and some data statistics.
  • /model contains model definitions, scripts to run models and a performance evaluation pipeline.
  • /result contains summary plots for model performance.
  • /report contains latex source and pdf rendering of project report

Requirements

Scripts have been tested under the following environment:

  • python 3.6.3
    • numpy 1.12.1
    • scipy 1.1.0
    • sklearn 0.19.1
    • tensorflow 1.8.0
    • h5py 2.7.1
    • tqdm 4.23.4
  • R 3.4.3
    • rhdf5 2.22.0
    • Biostrings 2.46.0
    • pROC 1.12.1
    • mccr 0.4.4
    • dplyr 0.7.4
    • reshape2 1.4.3
    • ggplot2 2.2.1

plocnet's People

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