Utilize automated machine learning to predict gene synthesis plant specialized metabolites
- System requirements
- Installation guide
- Demo
- Instructions for use
- License
- Reference
The software was tested on CentOS 7.5.1804 (Core) with Miniconda3. Other packages were used as follows:
python 3.8.12
autogluon 0.3.1
numpy
scipy
pandas
Install miniconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.sh
Create a new conda environment and update
conda create --name autogluon python==3.8.12
conda update -n base conda
Activate conda environment
conda activate autogluon
Install dependencies:
pip install -U setuptools wheel
pip install autogluon==0.3.1
Install ML-pre-GSPSM:
git clone https://github.com/baiwenhuim/ML-pre-GSPSM.git
The directory including in model script is changed into:
cd /ML-pre-GSPSM/model
And you can also download the pre-trained model in three species (Arabidopsis thaliana, maize, and tomato) from Zenodo.
Once downloaded, you can unzip it:
unzip azs.zip
Then, you can use the script predict_unknown_gene.py to predict enzyme genes which you want to predict:
python predict_unknown_gene.py --path /prefix/model_path/azs --input /prefix/input_file.csv --output output.csv
The following results are output into"/Downloaded_directory_path/output.csv":
● Cross validation results in the basic models and AutoGluon-Tabular
● Test results in the AutoGluon-Tabular mode.
Run time depends on the dataset size.
This software is released under the MIT License, according to LICENSE.