Data mining in genomics and proteomics studies reveals new regulatory pathways and mechanisms in different health and disease conditions as presented by Wren and Garner, and provides comparative sequence analysis approaches as presented by Gambin and Otto and Gao et al. Those studies have also provided approaches for subcellular localization of proteins suggesting that such approaches can produce an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization as presented by Chen and Murphy. Chen et al studied the performance of five nonparameteric tests to select genes and proved that the popular F test does not perform well on gene expression data since the heterogeneity behavior assumption is the most dominant in the gene expression data. Corder et al explored a statistical approach called grade of membership (GOM) and proved that brain hypoperfusion contributes to dementia, possibly to Alzheimer's disease (AD) pathogenesis, and raises the possibility that the APOE ϵ4 allele contributes directly to heart value and myocardial damage. Hand and Heard present in their review article various tools for finding relevant subgroups in gene expression data. Alkharouf et al conduct an OLAP cube (online analytical processing) to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, which is a devastating pest of soybean.