Genetic programming for classification and feature selection: Analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies

Helen F. Gray, Ross J. Maxwell, Irene Martínez-Pérez, Carles Arús, Sebastían Cerdán

Research output: Contribution to journalArticleResearchpeer-review

47 Citations (Scopus)

Abstract

Genetic programming (GP) is used to classify tumours based on 1H nuclear magnetic resonance (NMR) spectra of biopsy extracts. Analysis of such data would ideally give not only a classification result but also indicate which parts of the spectra are driving the classification (i.e. feature selection). Experiments on a database of variables derived from 1H NMR spectra from human brain tumour extracts (n = 75) are reported, showing GP's classification abilities and comparing them with that of a neural network. GP successfully classified the data into meningioma and non-meningioma classes. The advantage over the neural network method was that it made use of simple combinations of a small group of metabolites, in particular glutamine, glutamate and alanine. This may help in the choice of the most informative NMR spectroscopy methods for future non-invasive studies in patients.
Original languageEnglish
Pages (from-to)217-224
JournalNMR in Biomedicine
Volume11
Issue number4-5
DOIs
Publication statusPublished - 1 Jun 1998

Keywords

  • Artificial intelligence
  • Brain tumour
  • Classification
  • Feature selection
  • Genetic programming

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