Premium
Genetic programming for classification and feature selection: analysis of 1 H nuclear magnetic resonance spectra from human brain tumour biopsies
Author(s) -
Gray Helen F.,
Maxwell Ross J.,
MartínezPérez Irene,
Arús Carles,
Cerdán Sebastián
Publication year - 1998
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/(sici)1099-1492(199806/08)11:4/5<217::aid-nbm512>3.0.co;2-4
Subject(s) - nuclear magnetic resonance , feature selection , feature (linguistics) , genetic programming , magnetic resonance imaging , selection (genetic algorithm) , computational biology , pathology , pattern recognition (psychology) , biology , artificial intelligence , computer science , medicine , physics , radiology , linguistics , philosophy
Abstract 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. © 1998 John Wiley & Sons, Ltd.