Comparison of Bayesian and regression models in missing enzyme identification

Bo Geng, Xiaobo Zhou, Y. S. Hung, Stephen Wong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Computational identification of missing enzymes is important in metabolic network reconstruction. For a metabolic reaction, given a set of candidate enzymes identified by biological evidences, a powerful predictive model is necessary to predict the actual enzyme(s) catalysing the reaction. In this study, we compare Bayesian Method, which is used in previous work, with several regression models. We apply the models to known reactions in E. coli and three other bacteria. It is shown that the proposed regression models obtain favourable performance when compared with the Bayesian method.

Original languageEnglish (US)
Pages (from-to)363-374
Number of pages12
JournalInternational Journal of Bioinformatics Research and Applications
Issue number4
StatePublished - Nov 2008


  • Bayesian model
  • Metabolic network
  • Missing enzymes identification
  • Regression

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Biomedical Engineering
  • Clinical Biochemistry


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