Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies
✍ Scribed by Helen F. Gray; Ross J. Maxwell; Irene Martínez-Pérez; Carles Arús; Sebastián Cerdán
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 150 KB
- Volume
- 11
- Category
- Article
- ISSN
- 0952-3480
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✦ Synopsis
Genetic programming (GP) is used to classify tumours based on 1 H 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 1 H 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.