PPI_Generative

This is the readme file that contains the guidelines and information about the compilation of the code of the following paper

Paper Name:- Protein-protein Interaction based Generative Model for Improving Gene Clustering

• Authors: Pratik Dutta¹, Sanket Pai², Aviral Kumar² and Sriparna Saha¹
• Affiliation: ¹Department of Computer Science and Engineering, IIT Patna, India, ²Depatment of Chemical Science and Technology, IIT Patna, India
• Accepted:(20/12/2019) Scientific Reports-Nature

If you consider this work as useful, please cite it as

@article{pratik2020protein,
  title={A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering},
  author={Pratik, Dutta and Sriparna, Saha and Sanket, Pai and Aviral, Kumar},
  journal={Scientific Reports (Nature Publisher Group)},
  volume={10},
  number={1},
  year={2020},
  publisher={Nature Publishing Group}
}

This repository contains code a protein-protein interaction-based generative model that can efficiently perform a gene clustering. In this work, protein-protein interaction(PPI) information is used as a latent variable of the generative model. The proposed generative model is fusion of protein interaction with Snorkel.

Getting Started

These instructions will allow you to emulate the results obtained from the code for development and testing purposes.

Prerequisites

• Python 2.7+ {For MOO-based clustering.}
• Python 3.6 {For developing generative model}
• sklearn
• matplotlib 2.0+
• mpl_toolkits
• numpy 1.10+
• Snorkel

Setup

All our code is implemented in Python. The installation instructions are as follows:

git clone
python get-pip.py
pip install numpy
pip install matplotlib
pip install numpy scipy scikit-learn

If the above doesn't work try upgrading the setup tools as follows -

pip install --upgrade setuptools

NOTE: To install Snorkel follow the given steps or refer here . This section is meant to help setup Snorkel on your systems. For more detailed instructions see the Installation section. These instructions assume that you already have conda installed. First, download and extract a copy of the Snorkel directory from a GitHub release (version 0.7.0 or greater). Then navigate to the root of the snorkel directory in a terminal and run the following:

install the environment
conda env create --file=environment.yml
activate the environment
source activate snorkel
install snorkel in the environment
pip install

Description

1. MOO-based Clustering

The details procedure for running the MOO-based clustering is described in the our GitHub repository.

2. Generative Model

The proposed generative model utilizes protein interaction information as the weighted factor for each solution. This folder contains additional files that helps to integrate the protein interaction information with the generative model. The generative model is obtained from the Snorkel. To run the code, please activate Snorkel environment. Then download the following files and store in the main folder

• panther.py This file is used to generate additional labels from GO-based solutions which is incorporated along with MOO-based solutions in order to increase the biological significance.

• ppi_in.py TThis file is used to process the data obtained from the protein - protein Interaction database which is used to generate weights which represent the accuracy of the weak supervision sources.

• PPI_gen_model.ipynb Protein interaction based modified implementation of the generative model. This modified generative model takes both MOO-based clustering and GO-based solutions along with the protein interaction information to generate final labels.

Authors

• Pratik Dutta (Ph.D, Indian Institute of Technology Patna)
• Sanket Pai (B.Tech, Indian Institute of Technology Patna)
• Aviral Kumar (B.Tech Indian Institute of Technology Patna)
• Dr. Sriparna Saha(Associate professor, Indian Institute of Technology Patna)

Contribution

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Also you can contact to the corresponding author Pratik Dutta (pratik.pcs16@iitp.ac.in )