Background: The study of metabolic pathway is one of the most important fields in
biochemistry. Good comprehension of the metabolic pathway system is helpful to uncover the
mechanism of some fundamental biological processes. Because chemicals are part of the main
components of the metabolic pathway, correct identification of which metabolic pathways a given
chemical can participate in is an important step for understanding the metabolic pathway system.
Most previous methods only considered the chemical information, which tried to deal with a multilabel
classification problem of assigning chemicals to proper metabolic pathways.
Methods: In this study, the pathway information was also employed, thereby transforming the
problem into a binary classification problem of identifying the pair of chemicals and metabolic
pathways, i.e., a chemical and a metabolic pathway was paired as a sample to be considered in this
study. To construct the prediction model, the association between chemical pathway type pairs was
evaluated by integrating the association between chemicals and association between pathway types.
The support vector machine was adopted as the prediction engine.
Results: The extensive tests show that the constructed model yields good performance with total
prediction accuracy around 0.878.
Conclusion: The comparison results indicate that our model is quite effective and suitable for the
identification of whether a given chemical can participate in a given metabolic pathway.