NDLI: Bootstrap Inference with Neural-Network Modeling for Gene-Disease Association Testing

Content Provider	IEEE Xplore Digital Library
Author	Matchenko-Shimko, N. Dube, M.P.
Copyright Year	2006
Description	Author affiliation: Univ. de Montreal, Que. (Matchenko-Shimko, N.; Dube, M.P.)
Abstract	Estimates derived from neural network modeling are used to test the significance of single nucleotide polymorphisms (SNPs) in the categorization of case control status in genetic association studies. Our artificial neural network (ANN) model of gene-disease correlation is represented by a fully connected 3-layered feedforward neural network with input nodes, corresponding to the number of studied SNPs, a hidden layer and a single output unit for the disease status. We used an evaluation procedure that measures the predictive significance of a single SNP, based on the change in the error function when the input is removed from the network. Two ANNs, one with all inputs and the other with a tested input removed are run in parallel and the change in error is calculated as a function of the relative out-of-sample performance of these two networks. With the help of a bootstrap technique (resampling with replacement) the valid inference of the tested input variables is derived. We report on the performance of the procedure as evaluated on simulated SNP datasets of varying complexity, on a real study dataset and in comparison to an SVM implementation of the procedure
Starting Page	1
Ending Page	7
File Size	215561
Page Count	7
File Format	PDF
ISBN	1424406234
DOI	10.1109/CIBCB.2006.330950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Diseases Artificial neural networks Genetics Neural networks Support vector machines Frequency Genomics Bioinformatics Pattern recognition
Content Type	Text
Resource Type	Article

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