Abstract
Feature selection has been an important issue for classification of proteomic mass spectra data since researchers are often interested in identifying potentially important biomarkers. In this study, a segmentation approach is adopted to locate the potential biomarker regions from the possible m/z range. Illustration is through real prostate cancer proteomic mass spectra data.
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Kuo, HC., Yeh, ST. (2015). Segmentation Based Feature Selection on Classifying Proteomic Spectral Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_11
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DOI: https://doi.org/10.1007/978-3-319-19369-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19368-7
Online ISBN: 978-3-319-19369-4
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