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.
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