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Data mining in proteomic mass spectrometry

Abstract

Data mining application to proteomic data from mass spectrometry has gained much interest in recent years. Advances made in proteomics and mass spectrometry have resulted in considerable amount of data that cannot be easily visualized or interpreted. Mass spectral proteomic datasets are typically high dimensional but with small sample size. Consequently, advanced artificial intelligence and machine learning algorithms are increasingly being used for knowledge discovery from such datasets. Their overall goal is to extract useful information that leads to the identification of protein biomarker candidates. Such biomarkers could potentially have diagnostic value as tools for early detection, diagnosis, and prognosis of many diseases. The purpose of this review is to focus on the current trends in mining mass spectral proteomic data. Special emphasis is placed on the critical steps involved in the analysis of surface-enhanced laser desorption/ionization mass spectrometry proteomic data. Examples are drawn from previously published studies and relevant data mining terminology and techniques are exlained.

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Correspondence to Saeed A. Jortani.

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Thomas, A., Tourassi, G.D., Elmaghraby, A.S. et al. Data mining in proteomic mass spectrometry. Clin Proteom 2, 13–32 (2006). https://doi.org/10.1385/CP:2:1:13

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Keywords

  • Data Mining
  • Feature Selection
  • Linear Discriminant Analysis
  • Receiver Operating Characteristic
  • Receiver Operating Characteristic Curve