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Processing and classification of protein mass spectra

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Published in Mass Spectrometry Reviews. 2006, vol. 25, no. 3, p. 409-449
Abstract Among the many applications of mass spectrometry, biomarker pattern discovery from protein mass spectra has aroused considerable interest in the past few years. While research efforts have raised hopes of early and less invasive diagnosis, they have also brought to light the many issues to be tackled before mass‐spectra‐based proteomic patterns become routine clinical tools. Known issues cover the entire pipeline leading from sample collection through mass spectrometry analytics to biomarker pattern extraction, validation, and interpretation. This study focuses on the data‐analytical phase, which takes as input mass spectra of biological specimens and discovers patterns of peak masses and intensities that discriminate between different pathological states. We survey current work and investigate computational issues concerning the different stages of the knowledge discovery process: exploratory analysis, quality control, and diverse transforms of mass spectra, followed by further dimensionality reduction, classification, and model evaluation. We conclude after a brief discussion of the critical biomedical task of analyzing discovered discriminatory patterns to identify their component proteins as well as interpret and validate their biological implications.
Keywords MS preprocessingClassificationBiomarker discoveryData miningProteomicsMachine learningDimensionality reduction
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Other version: http://doi.wiley.com/10.1002/mas.20072
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Research group Geneva Artificial Intelligence Laboratory
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HILARIO, Mélanie et al. Processing and classification of protein mass spectra. In: Mass Spectrometry Reviews, 2006, vol. 25, n° 3, p. 409-449. https://archive-ouverte.unige.ch/unige:120444

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Deposited on : 2019-07-08

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