Recently, we proposed a genetic network inference method using linear programming machines (LPMs). As this method infers genetic networks by solving linear programming problems, its computational time is very short. However, generic networks inferred by the method using the LPMs often contain a large number of false-positive regulations. When we try to apply the inference method to actual problems, we must experimentally validate the inferred regulations. Therefore, it is important to reduce the number of false-positive regulations. To decrease the number of regulations we must validate, this study assigns confidence values to all of the possible regulations. For this purpose, we combine a bootstrap method and the method using the LPMs. Through numerical experiments on artificial genetic network inference problems, we check the effectiveness of assessing the confidence values of the regulations.
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