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Lookup NU author(s): Dr Gajanan Sherbet,
Dr Wai Lok Woo,
Professor Satnam Dlay
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© 2018 International Institute of Anticancer Research. All rights reserved. Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis. The predictive ability of the cellular markers, DNA ploidy and cell-cycle profiles, and molecular markers, such as tumour promoter and suppressor gene, and growth factor and steroid hormone receptors in breast cancer management were also analysed. ANN systems have been successfully deployed to evaluate microRNA profiles of tumours which saliently sway cancer progression and prognosis of the disease, thus counteracting the negative implications of their numerical abundance. Finally, in this setting, the prospective technical improvements in artificial neural networks, as hybrid systems in combination with fuzzy logic and artificial immune networks were also addressed.
Author(s): Sherbet GV, Woo WL, Dlay S
Publication type: Review
Publication status: Published
Journal: Anticancer Research
Print publication date: 01/12/2018
Acceptance date: 02/11/2018
ISSN (print): 0250-7005
ISSN (electronic): 1791-7530
Publisher: International Institute of Anticancer Research