Inference of genetic networks using linear programming machines: Application of a priori knowledge

Recently, the inference of genetic networks was defined as a series of discrimination tasks. The inference method based on this problem definition infers genetic networks by obtaining predictors that can predict the signs of the differential coefficients of the gene expression levels. As these predictors are obtained by solving linear programming problems, the computational time of the method is very short. The method however has no explicit mechanism to utilize a priori knowledge about genetic networks. This study therefore extends the inference method based on the discrimination tasks to make it possible to utilize the a priori knowledge. In order to verify its effectiveness, we then apply the modified method to artificial genetic network inference problems.

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