Welcome to pyEnGNet’s documentation!

Deployment & Documentation & Stats

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Abstract here

pyengnet is featured for:

  • Unified APIs, detailed documentation, and interactive examples available to the community.

  • Complete coverage for reconstruction of massive gene co-expression networks.

  • Optimized models to generate results in the shortest possible time.

  • Optimization of a High-Performance Computing (HPC) and Big Data ecosystem, using cuda and multiprocess.

API Demo:

import os
from pyengnet.File import File
from pyengnet.Engnet import Engnet

if __name__ == "__main__":

   # Load dataset
   dataset = File.load(path=os.getcwd()+"/datasets/Spellman.csv", separator=",", nmi_th=0.6, spearman_th=0.7, kendall_th=0.7, readded_th=0.7, hub_th = 3)

   # Run pyEnGNet on CPUs
   graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True)

   # Run pyEnGNet on GPU devices
   # graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True, numGpus = 2, computeCapability = 61)

   # Save gene co-expression networks and additional information
   File.saveFile(path='/home/principalpc/Escritorio/graphComplete.csv',graph=infoGraphComplete) # Full network
   File.saveFile(path='/home/principalpc/Escritorio/graphFiltered.csv',graph=infoGraphFiltered) # Filtered network

   # Print gene co-expression networks
   File.showGraph(graph=graphComplete,title='Complete graph') # Full network
   File.showGraph(graph=graphFiltered,title="Filtered graph") # Filtered network

Citing pyEnGNet:

pyEnGNet paper is published in (under review). If you use pyEnGNet in a scientific publication, we would appreciate citations to the following paper:

Under review


Under review

Key Links and Resources:

Indices and tables