deep learning approach to classify animal toxins
- Get the repository for toxify
git clone https://github.com/tijeco/toxify.git
- Change into toxify repository
cd toxify
- Create conda environment with dependencies needed to run toxify
conda env create -f requirements.yml
- Activate toxify conda environment
source activate toxify_env
- Install toxify to the toxify conda environment
python setup.py install
- Run toxify
toxify predict <input.fasta>
- view results in
<input.fasta>_toxify_predictions/predictions_proteins.csv
- Deactivate toxify conda environment
source deactivate
If you wish to train a different model with your own training data, that can be accomplished with toxify using the train
sub command.
There are a few variables that can be modified in terms of setting up the model.
-
-pos
can be followed by a list of protein sequence fasta files that you wish to constitute the positive dataset -
-neg
can be followed by a list of protein sequence fasta files that you wish to constitute the negative dataset -
-maxLen
is the maximum length (integer) of protein seqeunce to be included in the training set -
-units
is the number (integer) of gated recurrent units to be used in the model -
-epochs
is the number (integer) of training epochs to run for the model -
lr
is the learning_rate (float, 0 <lr
< 1)