Rule based functional description of genes – Estimation of the multicriteria rule interestingness measure by the UTA method

In this paper we present new extension of RuleGO rule generation method. The method was designed to discover logical rules including combination of GO terms in their premises in order to provide functional description of analyzed gene signatures. As the number of obtained rules is typically huge, filtration algorithm is required to select only the most interesting ones. Rule interestingness measures currently used within the RuleGO method do not always allow for the selection of the rules according to user's subjective preferences. In this paper we propose an application of the UTA method for estimation of the multicriteria rule interestingness measure reflecting expert's subjective rule evaluation. In the presented method, each of the rules is characterized by a vector of values reflecting its quality due to the different parial interestingness measures. From the designated set of rules a set of representative rules is selected and presented to an expert who orders the rules based on his preferences. Using the information about the order and values of the partial interestingness measures, the additive multicriteria interestingness measure is estimated. The measure is estimated in such a way that the rule ranking obtained by this function is consistent with the ranking given by an expert. The presented approach is applied to three microarray data sets and obtained rule orders are compared with rule orders generated with the standard RuleGO rule evaluation method. Presented method allows obtaining the rule ranking that is better correlated with expert ranking than the ranking obtained in the standard way.