A major problem associated with the reverse engineering of genetic networks from micro-array data is how to reliably find genetic interactions when faced with a relatively small number of arrays compared to the number of genes. To cope with this dimensionality problem, it is imperative to employ additional (biological) knowledge about genetic networks, such as limited connectivity, redundancy, stability and robustness, to sensibly constrain the modeling process. Recently, we have shown that by applying single criteria, the inference of genetic interactions under realistic conditions can be significantly improved. In this paper, we study the problem of how to combine constraints by formulating it as a multi-criterion optimization problem.
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