Deployment & Documentation & Stats
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 networkCiting 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
or:
Under review
Key Links and Resources:
.. toctree:: :maxdepth: 2 :caption: Getting Started install examples
.. toctree:: :maxdepth: 2 :caption: Documentation api
.. toctree:: :maxdepth: 2 :caption: Additional information about faq release