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Convolutional neural network based prediction model for gene essentiality prediction in microbes
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AnalyzeParameter.py
DNNModel.py
DatasetProcessing.py
FeatureGeneration.py
FileProcessing.py
README.md
dataset.txt
main.py
orthoMCL.txt
sample_output.tab

README.md

DeeplyEssential

DeeplyEssential is a Convolutional neural network for the identification of Essential genes in bacteria species. The dataset used for the learning of the nework contains 30 bacterial species collected from DEG.

Dependency

  1. Python 2.7
  2. keras==2.1.5
  3. numpy==1.14.2
  4. pandas==0.22.0
  5. scikit-learn==0.19.1
  6. tensorflow==1.6.0

Parameters

DeeplyEssential takes 6 parameters

  1. Essential gene directory path. The directory contains
    • A essential gene sequence file
    • A essential protein sequence file
    • An gene annotation file
  2. Non Essential gene directory path. This file contains
    • A essential gene sequence file
    • A essential protein sequence file
    • An gene annotation file
  3. Clustered gene file path clusted by OrthoMCL (sample given, orthoMCL.txt)
  4. Text file containing bacteria species information (sample given, dataset.txt)
  5. Experiment option
    • '-gp' for Gram Positive (GP) Dataset
    • '-gn' for Gram Negative (GN) Dataset
    • '-c' for GP + GN Dataset
  6. Name of the experiment

Run code

$ python main.py <essential gene dir> <non-essential gene dir> <cluster gene file> <dataset> -c <experiment name>

The dataset are collected from DEG.

Output

DeeplyEssential generates a report containing experiment name, basic statistics about the dataset and evaluation metics for each iteration of experiment. A sample (sample_output.tab) is provided.

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