An important emerging medical application domain for microarray technology is clinical decision support in the form of diagnosis of diseases. For this task, several computational methods ranging from statistical alternatives to more complex hybrid systems have been previously proposed in the literature. In this work we study the utilisation of several ensemble alternatives for the task of classifying microarray data by using prior knowledge known to be biologically relevant to the target disease. The experimental results using different datasets and several gene sets show that the proposal is able to outperform previous approaches by introducing diversity as different gene sets.