- author = "Helen Frances Gray",
- title = "Evolutionary computing techniques to aid the acquisition and analysis of nuclear magnetic resonance data",
- school = "Department of Computing, City University",
- year = "2007",
- address = "London, UK",
- month = jan,
- keywords = "genetic algorithms, genetic programming, NMR, Brain Cancer, multiple trees, multiclass, Spin-Echo, PROBEN, 1H spectra, MR, AI medicine, ANN, DMSO, TSP",
-
URL = "
https://openaccess.city.ac.uk/id/eprint/8519/",
-
URL = "
https://openaccess.city.ac.uk/id/eprint/8519/1/Evolutionary_computing_techniques_to_aid_the_acquisition_and_analysis_of_nuclear_magnetic_resonance_data.pdf",
-
URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440692",
- size = "136 pages",
-
abstract = "Evolutionary computation, including genetic algorithms
and genetic programming have taken the ideas of
evolution in biology and applied some of the
characteristics to problem solving. The survival of the
fittest paradigm allows a population of candidate
solutions to be modified by sexual and asexual
reproduction and mutation to come closer to solving the
problem in question without the necessity of having
prior knowledge of what a good solution looks like.
The increasing importance of Nuclear Magnetic Resonance Spectroscopy in medicine has created a demand for automated data analysis for tissue classification and feature selection. The use of artificial intelligence techniques such as evolutionary computing can be used for such data analysis.
This thesis applies the techniques of evolutionary computation to aid the collection and classification of Nuclear Magnetic Resonance spectroscopy data. The first section (chapters one and two) introduces Nuclear Magnetic Resonance spectroscopy and evolutionary computation and also contains a review of relevant literature. The second section focuses on classification. In the third chapter classification into two classes of brain tumors is undertaken. The fourth chapter expands this to classify tumours and tissues into more than two classes. Genetic Programming provided good solutions with relatively simple biochemical interpretation and was able to classify data into more than two classes at one time. The third section of the thesis concentrates on using evolutionary computation techniques to optimise data acquisition parameters directly from the Nuclear Magnetic Resonance hardware. Chapter five shows that Genetic Algorithms in particular are successful at suppressing signals from solvent while chapter six applies these techniques to find a way of enhancing the signals from metabolites important to the classification of brain tumours as found in chapter three. The final chapter draws conclusions as to the efficacy of evolutionary computation techniques applied to Nuclear Magnetic Resonance Spectroscopy.",
-
notes = "uk.bl.ethos.440692 ISNI: 0000 0001 3512 7528 DAIMI
Aarhus, Denmark. Gray Cancer Institute, Northwood, UK.
Barcelona and Madrid, Spain.
Supervisor: Peter Smith (City University)",
