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Feature Selection and Machine Learning with Mass Spectrometry Data

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Book cover Bioinformatics Methods in Clinical Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 593))

Abstract

Mass spectrometry has been used in biochemical research for a long time. However, its potential for discovering proteomic biomarkers using protein mass spectra has aroused tremendous interest in the last few years. In spite of its potential for biomarker discovery, it is recognized that the identification of meaningful proteomic features from mass spectra needs careful evaluation. Hence, extracting meaningful features and discriminating the samples based on these features are still open areas of research. Several research groups are actively involved in making the process as perfect as possible. In this chapter, we provide a review of major contributions toward feature selection and classification of proteomic mass spectra involving MALDI-TOF and SELDI-TOF technology.

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Authors and Affiliations

  1. Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA

    Susmita Datta & Vasyl Pihur

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  1. Susmita Datta
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  2. Vasyl Pihur
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  1. Inst. Patologia e Imunologia Molecular, Universidade do Porto, Rua Dr. Roberto Frias s/n, Porto, 4200-465, Portugal

    Rune Matthiesen

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Datta, S., Pihur, V. (2010). Feature Selection and Machine Learning with Mass Spectrometry Data. In: Matthiesen, R. (eds) Bioinformatics Methods in Clinical Research. Methods in Molecular Biology, vol 593. Humana Press. https://doi.org/10.1007/978-1-60327-194-3_11

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  • DOI: https://doi.org/10.1007/978-1-60327-194-3_11

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60327-193-6

  • Online ISBN: 978-1-60327-194-3

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