The NIH/NCI estimates that one out of 57 women will develop ovarian cancer during their lifetime. Ovarian cancer is 90 percent curable when detected early. Unfortunately, many cases of ovarian cancer are not diagnosed until advanced stages because most women do not develop noticeable symptoms. This paper presents an exhaustive identification of all potential biomarkers for the diagnosis of early-stage and/or recurrent ovarian cancer using a unique and comprehensive set of gene expression data. The data set was generated by Gene Logic Inc. from ovarian normal and cancerous tissues as well as non-ovarian tissues collected at the University of Minnesota by Skubitz et al. In particular, the paper shows the ability of a modified biclustering technique combined with sensitivity analysis of gene expression levels to identify all potential biomarkers found by prior studies as well as several more promising candidates that had been missed in the literature. Furthermore, unlike most prior studies, this work screens all candidate biomarkers using two additional techniques: immunohistochemical analysis and reverse transcriptase polymerase chain reaction.