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There is a need to design computational methods to support the prediction of gene regulatory networks (GRNs). Such models should offer both biologically meaningful and computationally accurate predictions which, in combination with other techniques, may improve large-scale integrative studies. This paper presents a new machine-learning method for the prediction of putative regulatory associations from expression data which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression data set. The results were statistically validated and compared with the relationships inferred by two machine-learning approaches to GRN prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory large-scale studies of automated identification of potentially relevant gene expression associations.