A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels.
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| http://dx.doi.org/10.1007/s11693-010-9058-z | DOI Listing |
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