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- W1594564779 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." @default.
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- W1594564779 date "2007-01-01" @default.
- W1594564779 modified "2023-09-26" @default.
- W1594564779 title "Discovering Biomarkers for Myocardial Infarction from SELDI-TOF Spectra" @default.
- W1594564779 cites W2051830951 @default.
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- W1594564779 doi "https://doi.org/10.1007/978-3-540-70981-7_65" @default.
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