Abstract
We describe a three-step procedure to separate patients with myocardial infarction from a control group based on SELDI-TOF mass spectra. The procedure returns features (“biomarkers”) that are strongly present in one of the two groups. These features should allow future subjects to be classified as at-risk of myocardial infarction. The algorithm uses morphological operations to reduce noise in the input data as well as for performing baseline correction. In contrast to previous approaches on SELDI-TOF spectra, we avoid black-box machine learning procedures and use only features (protein masses) that are easy to interpret.
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© 2007 Springer-Verlag Berlin Heidelberg
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Höner zu Siederdissen, C., Ragg, S., Rahmann, S. (2007). Discovering Biomarkers for Myocardial Infarction from SELDI-TOF Spectra. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_65
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DOI: https://doi.org/10.1007/978-3-540-70981-7_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
Online ISBN: 978-3-540-70981-7
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