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

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Predicting metabolic pathways for unannotated biomolecules improves metabolomics analysis. Using the Reactome knowledgebase for machine learning models significantly enhanced pathway prediction accuracy compared to previous methods.

Keywords:
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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • Pathway annotations of small biomolecules are crucial for interpreting metabolomics data.
  • Limited pathway annotation hinders comprehensive analysis and omics integration.
  • Previous predictive models relied solely on Kyoto Encyclopedia of Genes and Genomes (KEGG) datasets.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting pathway involvement of unannotated biomolecules.
  • To explore the utility of the Reactome knowledgebase for pathway prediction.
  • To improve metabolomics data interpretation and omics integration.

Main Methods:

  • Constructed a machine learning dataset from Reactome knowledgebase compounds.
  • Engineered metabolite-pathway paired feature vectors.
  • Trained and evaluated a multilayer perceptron binary classifier.

Main Results:

  • Models trained on Reactome data achieved a higher mean Matthew's correlation coefficient (MCC) of 0.916 compared to KEGG-based models (0.847).
  • The Reactome dataset encompasses a larger number of pathways (3985) than the KEGG dataset (502).
  • The developed models demonstrate effective prediction of pathway involvement using Reactome data.

Conclusions:

  • The Reactome knowledgebase is a valuable resource for developing accurate pathway prediction models.
  • Utilizing Reactome significantly expands the number of predictable human-defined pathways.
  • This approach enhances the potential for improved metabolomics data analysis and omics integration.