Mass spectrometry based proteomics technologies have allowed for a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, they face acute challenges from a data reproducibility standpoint, in that no two independent studies have been found to produce the same proteomic patterns. Such reproducibility issues cause the identified biomarker patterns to lose repeatability and prevent real clinical usage. In this work, we propose a profile biomarker approach to overcome this problem from a machine-learning viewpoint by developing a novel derivative component analysis (DCA)