Abstract
Mass Spectrometry (MS) can be used as a detector in High Performance Liquid Chromatography (HPLC) systems or as a tool for direct protein/peptides profiling from biological samples. Data Mining (DM) is the semi-automated extraction of patterns representing knowledge implicitly stored in large databases. The combined use of MS with DM is a novel approach in proteomic pattern analysis and is emerging as an effective method for the early diagnosis of diseases. We describe the workflow of a proteomic experiment for early detection of cancer which combines MS and DM, giving details of sample treatment and preparation, MS data generation, MS data preprocessing, data clustering and classification.
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Baudi, F. et al. (2005). Mass Spectrometry Data Analysis for Early Detection of Inherited Breast Cancer. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_3
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DOI: https://doi.org/10.1007/1-4020-3432-6_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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