Knowledge Discovery in Proteomic Mass Spectrometry Data

High-throughput technologies such as mass spectrometry produce large amounts of data that require sophisticated computational methods to preprocess and identify highly discriminatory features (biomarker candidates) from these data. In this chapter, a computational workflow for the search and identification of biomarker candidates using mass spectrometry data is presented. First, preprocessing steps necessary to transform raw spectra into comparable data sets are described, followed by a novel three-step feature selection approach that combines the advantages of efficient filter and effective wrapper techniques. The proposed workflow has been integrated into the Knowledge Discovery in Databases (KD 3 ) Designer tool, our self-designed and cost-free software package. One of the main advantages of this tool is its straightforward design, which visualizes processing steps that can be easily connected to workflows. Due to its modular software architecture, new algorithms can be readily implemented into the system. This analysis strategy was evaluated using an example mass spectrometry data set.