Computational protein biomarker prediction: a case study for prostate cancer
Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.
prostate cancer anova f-statistic b/w ratio biomarker discovery feature (peak) selection seld1-tof mass spectrometry statistical discrimination methods support vector machine.