Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase.

ABSTRACT: Background/Objectives: Pathway annotations of non-macromolecular (relatively small) biomolecules facilitate biological and biomedical interpretation of metabolomics datasets. However, low pathway annotation levels of detected biomolecules hinder this type of interpretation. Thus, predicting the pathway involvement of detected but unannotated biomolecules has a high potential to improve metabolomics data analysis and omics integration. Past publications have only made use of the Kyoto Encyclopedia of Genes and Genomes-derived datasets to develop machine learning models to predict pathway involvement. However, to our knowledge, the Reactome knowledgebase has not been utilized to develop these types of predictive models. Methods: We created a dataset ready for machine learning u

SUBMITTER: Huckvale ED 

PROVIDER: S-EPMC11944036 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature