Data mining is the search for hidden trends within large sets of data. Data mining approaches are needed at all levels of genomics and proteomics analyses. These studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens from healthy and diseased tissues. The high dimensionality of data generated from these studies will require the development of improved bioinformatics and computational biology tools for efficient and accurate data analyses.This issue of the Journal of Biomedicine and Biotechnology consists of seventeen papers that describe different applications of data mining to both genomics and proteomics studies in yeast, and plant and human cells and tissues. Papers by Bensmail et al, Ghosh and Chinnaiyan, and Mao et al present different classification and clustering approaches for disease biomarkers discovery. Genomics and proteomics studies have shown great promises and have been applied to studies aiming at generating expression profiles and elucidating expression networks in different organisms as shown in the papers by Samsa et al, Mungur et al, Liu et al, Baldwin et al, and Joy et al.