Development and evaluation of a prototype system for automated analysis of clinical mass spectrometry data

Abstract

Mass Spectrometry (MS) is emerging as a breakthrough mass-throughput technology believed to have powerful potential for producing clinical diagnostic and prognostic models and for identifying relevant disease biomarkers. A major barrier to making mass spectrometry clinically useful – and to exploring its potential in an efficient and reliable manner – is the challenge posed by data analysis of proteomic spectra in order to produce reliable predictor models of disease and clinical outcomes. This thesis describes the development and evaluation of a fully-automated software system (FAST-AIMS), capable of analyzing mass spectra to produce high-quality diagnostic and outcome prediction models.