One reason that ovarian cancer is such a deadly disease is because it is not usually diagnosed until it has reached an advanced stage. In this study, we developed a novel algorithm for group biomarkers identification using gene expression data. Group biomarkers consist of coregulated genes across normal and different stage diseased tissues. Unlike prior sets of biomarkers identified by statistical methods, genes in group biomarkers are potentially involved in pathways related to different types of cancer development. They may serve as an alternative to the traditional single biomarkers or combination of biomarkers used for the diagnosis of early-stage and/or recurrent ovarian cancer. We extracted group biomarkers by applying biclustering algorithms that we recently developed on the gene expression data of over 400 normal, cancerous, and diseased tissues. We identified several groups of coregulated genes that encode for secreted proteins and exhibit expression levels in ovarian cancer that are at least 2-fold (in log2 scale) higher than in normal ovary and nonovarian tissues. In particular, three candidate group biomarkers exhibited a conserved biological pattern that may be used for early detection or recurrence of ovarian cancer with specificity greater than 99% and sensitivity equal to 100%. We validated these group biomarkers using publicly available gene expression data sets downloaded from a NIH Web site (http://www.ncbi.nlm.nih.gov/geo). Statistical analysis showed that our methodology identified an optimum combination of genes that have the highest effect on the diagnosis of the disease compared with several computational techniques that we tested. Our study also suggests that single or group biomarkers correlate with the stage of the disease.