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.
CITATION STYLE
Höner Zu Siederdissen, C., Ragg, S., & Rahmann, S. (2007). Discovering biomarkers for myocardial infarction from SELDI-TOF spectra. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 569–576). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-70981-7_65
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