Among the large number of gene selection algorithms available in literature, the rough set based maximum relevance-maximum significance (RSMRMS) algorithm has been shown to be successful for selecting a set of relevant and significant genes from microarray data. However, the analysis of functional diversity of a gene set is essential to understand the role of genes in a particular disease as well as to evaluate the effectiveness of a gene selection algorithm. In this regard, a gene ontology based quantitative index, termed as degree of functional diversity (DoFD), is proposed to quantify the functional diversity of a set of genes selected by any gene selection algorithm. Moreover, a new gene selection algorithm is presented, integrating judiciously the merits of both DoFD and RSMRMS, to select relevant and significant genes those are also functionally diverse. The performance of the proposed gene selection algorithm, along with a comparison with other gene selection methods, is studied using the proposed DoFD and predictive accuracy of K-nearest neighbor rule and support vector machine on six cancer and one arthritis microarray data sets. An important finding is that the proposed gene ontology based quantitative index can accurately evaluate functional diversity of a set of genes. Also, the proposed gene selection algorithm is shown to be effective for selecting relevant, significant, and functionally diverse genes from microarray data. © 2012 Springer-Verlag.