Missing Enzyme Identification Using Reversible-Jump Markov-Chain-Monte-Carlo Learning Approach
نویسندگان
چکیده
Computational identification of missing enzymes plays a significant role in reconstruction of metabolic network. For a metabolic reaction, given a set of candidate enzymes identified by certain biological evidences, there is a need to develop a powerful mathematical model to predict the actual enzyme(s) catalyzing the reaction. In this study, a regression model is proposed to solve the problem, in which a reversible jump Markov-chain-Monte-Carlo learning technique is used to estimate the model parameters. We evaluate the model using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae, and Caulobacter cresentus. It is demonstrated that the model obtains favorable results compared with several other approaches.
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