Abstract: Although, advances in early stage detection of cancer have come of great help to cancer treatment, most routine screening and diagnosis tools lack sufficient sensitivity and specificity of molecular approaches such as proteomics. With the proteomic technologies emerging, classification and identification of body fluid proteins have been a major focus of scientists. Proteomic analyses have opened a new horizon in screening changes happening in cellular processes to become cancerous; however, it is yet to be perfected using complementary approaches for more accurate diagnosis of cancers. A combination of proteomics approaches like Ciphergen Protein Chip Arrays and SELDITOF MS with bioinformatics tools was proved to be effective in the discovery of new biomarkers which further helps the early-stage detection and diagnosis of cancer. In this study, the UMSA algorithm provided an efficient model to rank a large number of peaks collectively according to their contribution to the separation of two predefined diagnostic groups. The Pro Peak, BootStrap module introduced random perturbations in multiple runs to test the consistency of the top-ranked peaks, measured by the SD of computed ranks from multiple runs. To establish an upper cutoff value on a peak’s rank SD for its performance not to be considered as purely by chance, the same bootstrap procedure was applied to a randomly generated data set that simulated the distribution of the real data. The minimum value of rank SDs from such “simulated peaks” indicates the degree of consistency that a peak might achieve by random chance. This minimum value was used as the cutoff to help to reduce the original 147 peaks to a subset of 15 peaks for further consideration. The performance of such peaks should be less likely attributable to random artifacts in the data. For the three biomarkers selected, no significant correlation was found between the concentrations of the markers and tumor size or lymph node metastasis. The discriminatory power of these markers therefore most likely reflects the malignant nature of the tumor rather than its progression. The origin and identity of BC1, BC2, and BC3 are currently under investigation. Furthermore, it is not our intent at this stage to suggest a final diagnostic algorithm based on nonlinear classification. In conclusion, we have shown that using proteomics approaches such as Ciphergen ProteinChip Arrays and SELDI-TOF MS in combination with bioinformatics tools could facilitate the discovery of new biomarkers. Using the panel of three selected biomarkers, we could achieve high sensitivity and specificity for the detection of breast cancer.