Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/57110
Title: Predicting risk of coronary artery disease from DNA microarray-based genotyping using neural networks and other statistical analysis tool.
Authors: Tham, C.K. 
Heng, C.K.
Chin, W.C.
Issue Date: Oct-2003
Source: Tham, C.K.,Heng, C.K.,Chin, W.C. (2003-10). Predicting risk of coronary artery disease from DNA microarray-based genotyping using neural networks and other statistical analysis tool.. J Bioinform Comput Biol 1 (3) : 521-539. ScholarBank@NUS Repository.
Abstract: This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA) and factor analysis (FA). A description of the biological background of the study is given, followed by a detailed description of how the problem has been modeled for analyses by neural networks and FA. A committee learning approach for NN has been used to improve generalization rates. We show that our NN approach is able to yield promising prediction results despite using only the most fundamental network structures. More interestingly, through the statistical analysis process, genes of similar biological functions have been clustered. In addition, a gene marker involved in breaking down lipids has been found to be the most correlated to CAD.
Source Title: J Bioinform Comput Biol
URI: http://scholarbank.nus.edu.sg/handle/10635/57110
ISSN: 02197200
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

25
checked on Dec 15, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.