Comparison of Bayesian and regression models in missing enzyme identification

Authors:
Bo Geng;Xiaobo Zhou;Y. S. Hung;Stephen Wong
Affiliations:
The Methodist Hospital Research Institute, and Dept of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX/ Dept of Electrical and Electronic Engineering, Univ of Hong Kon ...;Center for Biotechnology and Informatics(/CBI)/, The Methodist Hospital Research Institute, and Dept of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX, 77030, U ...;Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong.;Center for Biotechnology and Informatics(/CBI)/, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX, 77 ...
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2008

Citing 2
Cited 0

Filling gaps in a metabolic network using expression information

Bioinformatics
Robust Full Bayesian Learning for Radial Basis Networks

Neural Computation

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Abstract

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.