Optimization of neural network architecture using
genetic programming improves detection and modeling of
gene-gene interactions in studies of human diseases
Background Appropriate definition of neural network
architecture prior to data analysis is crucial for
successful data mining. This can be challenging when
the underlying model of the data is unknown. The goal
of this study was to determine whether optimizing
neural network architecture using genetic programming
as a machine learning strategy would improve the
ability of neural networks to model and detect
nonlinear interactions among genes in studies of common
human diseases.
Results Using simulated data, we show that a genetic
programming optimized neural network approach is able
to model gene-gene interactions as well as a
traditional back propagation neural network.
Furthermore, the genetic programming optimized neural
network is better than the traditional back propagation
neural network approach in terms of predictive ability
and power to detect gene-gene interactions when
non-functional polymorphisms are present. Conclusion
This study suggests that a machine learning strategy
for optimizing neural network architecture may be
preferable to traditional trial-and-error approaches
for the identification and characterization of
gene-gene interactions in common, complex human
diseases.
%0 Journal Article
%1 Ritchie:2003:BMCB
%A Ritchie, Marylyn D.
%A White, Bill C.
%A Parker, Joel S.
%A Hahn, Lance W.
%A Moore, Jason H.
%D 2003
%J BMC Bioinformatics
%K algorithms, genetic programming
%N 28
%R doi:10.1186/1471-2105-4-28
%T Optimization of neural network architecture using
genetic programming improves detection and modeling of
gene-gene interactions in studies of human diseases
%U http://www.biomedcentral.com/1471-2105/4/28
%V 4
%X Background Appropriate definition of neural network
architecture prior to data analysis is crucial for
successful data mining. This can be challenging when
the underlying model of the data is unknown. The goal
of this study was to determine whether optimizing
neural network architecture using genetic programming
as a machine learning strategy would improve the
ability of neural networks to model and detect
nonlinear interactions among genes in studies of common
human diseases.
Results Using simulated data, we show that a genetic
programming optimized neural network approach is able
to model gene-gene interactions as well as a
traditional back propagation neural network.
Furthermore, the genetic programming optimized neural
network is better than the traditional back propagation
neural network approach in terms of predictive ability
and power to detect gene-gene interactions when
non-functional polymorphisms are present. Conclusion
This study suggests that a machine learning strategy
for optimizing neural network architecture may be
preferable to traditional trial-and-error approaches
for the identification and characterization of
gene-gene interactions in common, complex human
diseases.
@article{Ritchie:2003:BMCB,
abstract = {Background Appropriate definition of neural network
architecture prior to data analysis is crucial for
successful data mining. This can be challenging when
the underlying model of the data is unknown. The goal
of this study was to determine whether optimizing
neural network architecture using genetic programming
as a machine learning strategy would improve the
ability of neural networks to model and detect
nonlinear interactions among genes in studies of common
human diseases.
Results Using simulated data, we show that a genetic
programming optimized neural network approach is able
to model gene-gene interactions as well as a
traditional back propagation neural network.
Furthermore, the genetic programming optimized neural
network is better than the traditional back propagation
neural network approach in terms of predictive ability
and power to detect gene-gene interactions when
non-functional polymorphisms are present. Conclusion
This study suggests that a machine learning strategy
for optimizing neural network architecture may be
preferable to traditional trial-and-error approaches
for the identification and characterization of
gene-gene interactions in common, complex human
diseases.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Ritchie, Marylyn D. and White, Bill C. and Parker, Joel S. and Hahn, Lance W. and Moore, Jason H.},
biburl = {https://www.bibsonomy.org/bibtex/2c69bad282975f213637230a77de29bec/brazovayeye},
doi = {doi:10.1186/1471-2105-4-28},
interhash = {c120613a8b40c96bc7845726a8ab2693},
intrahash = {c69bad282975f213637230a77de29bec},
journal = {BMC Bioinformatics},
keywords = {algorithms, genetic programming},
month = {7 July},
number = 28,
size = {14 pages},
timestamp = {2008-06-19T17:50:16.000+0200},
title = {Optimization of neural network architecture using
genetic programming improves detection and modeling of
gene-gene interactions in studies of human diseases},
url = {http://www.biomedcentral.com/1471-2105/4/28},
volume = 4,
year = 2003
}