Scientific article

Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents

Published inProteomics, vol. 4, no. 8, p. 2320-2332
Publication date2004

In this paper we try to identify potential biomarkers for early stroke diagnosis using surface-enhanced laser desorption/ionization mass spectrometry coupled with analysis tools from machine learning and data mining. Data consist of 42 specimen samples, i.e., mass spectra divided in two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that correspond to ischemic and hemorrhagic stroke; in this paper we limit our data analysis to discriminating between control and stroke specimens. We performed two suites of experiments. In the first one we simply applied a number of different machine learning algorithms; in the second one we have chosen the best performing algorithm as it was determined from the first phase and coupled it with a number of different feature selection methods. The reason for this was 2-fold, first to establish whether feature selection can indeed improve performance, which in our case it did not seem to confirm, but more importantly to acquire a small list of potentially interesting biomarkers. Of the different methods explored the most promising one was support vector machines which gave us high levels of sensitivity and specificity. Finally, by analyzing the models constructed by support vector machines we produced a small set of 13 features that could be used as potential biomarkers, and which exhibited good performance both in terms of sensitivity, specificity and model stability.

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Biological Markers
  • Female
  • Gene Expression Profiling
  • Humans
  • Male
  • Mass Spectrometry/methods
  • Middle Aged
  • Protein Array Analysis
  • Sensitivity and Specificity
  • Stroke/blood/classification/diagnosis/pathology
Citation (ISO format)
PRADOS, Julien et al. Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents. In: Proteomics, 2004, vol. 4, n° 8, p. 2320–2332. doi: 10.1002/pmic.200400857
Main files (1)
Article (Published version)
ISSN of the journal1615-9853

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