This repo contains an implementation of triUMPF (triple non-negative matrix factorization with commUnity detection to Metabolic Pathway inFerence) that combines three stages of NMF to capture relationships between enzymes and pathways within a network followed by community detection to extract higher order structure based on the clustering of vertices sharing similar functional features. We evaluated triUMPF performance using experimental datasets manifesting diverse multi-label properties, including Tier 1 genomes from the BioCyc collection of organismal Pathway/Genome Databases and low complexity microbial communities. Resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved prediction outcomes on multi-organism data sets.
See tutorials on the GitHub wiki page for more information and guidelines.
If you find triUMPF useful in your research, please consider citing the following paper:
- M. A. Basher, Abdur Rahman, McLaughlin, Ryan J., and Hallam, Steven J.. "Metabolic Pathway Prediction Using Non-Negative Matrix Factorization with Improved Precision", Journal of Computational Biology (2021).
For any inquiries, please contact: arbasher@student.ubc.ca