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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/5486

Title: Studies - 'neural network' algorithm to predict severity in epidermolysis bullosa simplex
Authors: Eapen, Bell Raj
Keywords: Epidermolysis bullosa simplex, Neural network algorithm
Issue Date: 31-Dec-2005
Publisher: Medknow Publications on behalf of The Indian Association of Dermatologists, Venereologists and Leprologists (IADVL)
Citation: Indian Journal of Dermatology, Venereology and Leprology (ISSN: 0378-6323) Vol 71 Num 2
Abstract: BACKGROUND AND AIMS: There are various genotypic variations known for the four phenotypic presentations of epidermolysis bullosa simplex (EBS). A neural network algorithm may be used to find the relationship between the various factors responsible for a particular phenotypic presentation. We assessed the value of neural network to predict the prognosis of epidermolysis bullosa simplex. METHODS: Cases of EBS in which sequencing had been performed to identify the position and type of mutation were collected by literature search and the resulting data was analyzed using neural network algorithm. RESULTS: The statistical prediction had an accuracy rate of 78%. CONCLUSION: Neural networks can identify hidden patterns in a huge database without the intervention of a skilled statistician. It has the potential to change the way we analyze clinical and experimental data at present.
URI: http://hdl.handle.net/1807/5486
Other Identifiers: http://www.bioline.org.br/abstract?id=dv05033
Rights: Copyright 2005 Indian Journal of Dermatology, Venereology, Leprology.
Appears in Collections:Bioline International Legacy Collection

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