DocumentCode
3549365
Title
Prediction of type II MODY3 diabetes using backpercolation
Author
Khan, Nawaz ; Ikejiaku, Chukwuemeka A. ; Rahman, Shahedur
Author_Institution
Middllesex Univ., London, UK
fYear
2005
fDate
23-24 June 2005
Firstpage
401
Lastpage
403
Abstract
In this study, a neural network based approach is used to predict the presence of Maturity Onset Diabetes type 3, referred as MODY3 type II diabetes mellitus. The study has used backpercolation neural network algorithm to predict the specific genetic mutation that causes the MODY3 type II diabetes mellitus. A set of coded numeric values are assigned for numeric representation of genetic data that are available in public domain repositories. A point mutation is introduced in a portion of the nucleotide for the mutation prediction to train the data set. The study has demonstrated that backpercolation neural network algorithm is useful to train and to predict gene point mutation that leads to MODY3 type II diabetes.
Keywords
diseases; feedforward neural nets; genetics; learning (artificial intelligence); medical computing; backpercolation; gene point mutation; genetic data; genetic mutation; maturity onset diabetes type 3; neural network algorithm; nucleotide; public domain repository; type II MODY3 diabetes mellitus; Computer errors; Computer networks; Diabetes; Diseases; Feedforward neural networks; Genetic mutations; Network topology; Neural networks; Neurons; Prediction algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on
ISSN
1063-7125
Print_ISBN
0-7695-2355-2
Type
conf
DOI
10.1109/CBMS.2005.85
Filename
1467723
Link To Document