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). As an 
implicit feature selection algorithm, derivative component analysis enables the 
separation of true signals from red herrings by capturing subtle data behaviors 
and removing system noises from a proteomic profile. We further demonstrate its 
advantages in disease diagnosis by viewing input data as a profile biomarker. 
The results from our profile biomarker diagnosis suggest an effective solution 
to overcoming proteomics data's reproducibility problem, present an alternative 
method for biomarker discovery in proteomics, and provide a good candidate for 
clinical proteomic diagnosis.