Metabolic pathway inference using multi-label classification with rich pathway features.

ABSTRACT: Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabolic relationships within and between cells. Here, we presen

SUBMITTER: M A Basher AR 

PROVIDER: S-EPMC7529316 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature