MLEssential

MLEssential is a computational toolbox to predict essential genes for yeast species using machine learning based on multi-scale information, including sequence features and evolution-based features.

Dependencies

• Python==3.7.4
• biopython==1.75
• scikit-learn==0.22.1
• numpy==1.17.2
• scipy==1.3.1
• pandas==0.25.1
• seaborn==0.9.0

Schematic workflow for gene essentiality prediction using machine learning methods

Citation

Please cite this paper: Lu, H. et al. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Molecular Systems Biology 2021(17):e10427. https://www.embopress.org/doi/full/10.15252/msb.202110427.

Contributors

• Le Yuan (@leyuan), Chalmers University of Technology, Sweden
• Hongzhong Lu (@hongzhonglu), Chalmers University of Technology, Sweden
• Feiran Li (@feiranl), Chalmers University of Technology, Sweden
• Eduard J. Kerkhoven (@edkerk), Chalmers University of Technology, Sweden