Abstract
A biomarker selection system is proposed for identifying biomarkers related to prostate cancer. MS-spectra were obtained from the National Cancer Institute Clinical Proteomics Database. The system comprised two stages, a pre-processing stage, which is a sequence of MS-processing steps consisting of MS-spectrum smoothing, novel iterative peak selection, peak alignment, and a classification stage employing the PNN classifier. The proposed iterative peak selection method was based on first applying local thresholding, for determining the MS-spectrum noise level, and second applying an iterative global threshold estimation algorithm, for selecting peaks at different intensity ranges. At each global threshold, an optimum sub-set of these peaks was used to design the PNN classifier for highest performance, in discriminating normal cases from cases with prostate cancer, and thus indicate the best m/z values. Among these values, the information rich biomarkers 1160.8, 2082.2, 3595.9, 4275.3, 5817.3, 7653.2, that have been associated with the prostate gland, are proposed for further investigation.
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Bougioukos, P. et al. (2007). Biomarker Selection System, Employing an Iterative Peak Selection Method, for Identifying Biomarkers Related to Prostate Cancer. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_25
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DOI: https://doi.org/10.1007/978-3-540-74272-2_25
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