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Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

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poncey/PreAntiCoV

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PreAntiCOV

Glimpse

An implementation for prediction and analysis of Anti-CoV functional peptide.

How to Use

  • analysis.ipynb: contains basic feature description of AAC, PHYCs, as well as classification between Anti-CoV and the other from three different sets
  • classify.py: Make classification for different functional peptides set.
  • ArgsClassify.py: Parameters of how to perform classification.

Requirements

We have already integrate the environment in env.yml. execute conda create -f env.yml to install packages required in a new created AMPrediction conda env.

Enter the enviornment with conda activate AMPrediction.

Steps for constructing classifiers

  1. To extract features for given .fasta files, execute python feature_extract.py.
  2. To establish predictors, execute classify.py. You can adjust any parameters at ArgsClassify.py.

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Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

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