title: A novel profile biomarker diagnosis for mass spectral proteomics.
abstract: 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.
authors.affiliation: Department of Computer and Information Science, Fordham University, New York, NY 10023, USA. xhan9@fordham.edu.
authors.forename: Henry
authors.initials: H
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000032:analysis*; D019295:Computational Biology; D057225:Data Mining / Q000706:statistics & numerical data; D030562:Databases, Protein / Q000706:statistics & numerical data; D006801:Humans; D013058:Mass Spectrometry / Q000706:statistics & numerical data; D025341:Principal Component Analysis; D040901:Proteomics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D058067:Wavelet Analysis
chemical_list: D015415:Biomarkers