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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{gray:1998:GPcfs:aNMRshbtb,

• author = "Helen F. Gray and Ross J. Maxwell and Irene Martinez-Perez and Carles Arus and Sebastian Cerdan",
• title = "Genetic programming for classification and feature selection: analysis of {1H} nuclear magnetic resonance spectra from human brain tumour biopsies",
• journal = "NMR Biomedicine",
• year = "1998",
• volume = "11",
• number = "4-5",
• pages = "217--224",
• month = jun # "-" # aug,
• keywords = "genetic algorithms, genetic programming, brain tumour, artificial intelligence, classification, feature selection",
• ISSN = "1099-1492",
• DOI = "doi:10.1002/(SICI)1099-1492(199806/08)11:4/5%3C217::AID-NBM512%3E3.0.CO%3B2-4",
• size = "8 pages",
• 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.",
• notes = "PMID: 9719576, UI: 98384081 Computer Science Department, Arhus University, Denmark.",

}

Genetic Programming entries for Helen Gray Ross James Maxwell Irene Martinez-Perez Carles Arus Sebastian Cerdan

Citations