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Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH - BMC Bioinformatics (2003)

Bottom Line: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Program in Human Genetics and Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN, 37232-0700, USA. ritchie@phg.mc.vanderbilt.edu

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.

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Binary expression tree example of a GP solution. Thisfigure is an example of a possible computer program generated byGP. While the program can take virtually any form, we are usinga binary expression tree representation, thus we have shown thistype as an example.
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Figure 1: Binary expression tree example of a GP solution. Thisfigure is an example of a possible computer program generated byGP. While the program can take virtually any form, we are usinga binary expression tree representation, thus we have shown thistype as an example.

Mentions: To avoid stalling on local minima, machine learning methods suchas genetic programming [14] and genetic algorithms [15] have been explored. Genetic programming(GP) is a machine learning methodology that generates computer programsto solve problems using a process that is inspired by biologicalevolution by natural selection [16-20].Genetic programming begins with an initial population of randomlygenerated computer programs, all of which are possible solutionsto a given problem. This step is essentially a random search orsampling of the space of all possible solutions. An example of onetype of computer program, called a binary expression tree, is shownin Figure 1.


Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH - BMC Bioinformatics (2003)

Binary expression tree example of a GP solution. Thisfigure is an example of a possible computer program generated byGP. While the program can take virtually any form, we are usinga binary expression tree representation, thus we have shown thistype as an example.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC183838&req=5

Figure 1: Binary expression tree example of a GP solution. Thisfigure is an example of a possible computer program generated byGP. While the program can take virtually any form, we are usinga binary expression tree representation, thus we have shown thistype as an example.
Mentions: To avoid stalling on local minima, machine learning methods suchas genetic programming [14] and genetic algorithms [15] have been explored. Genetic programming(GP) is a machine learning methodology that generates computer programsto solve problems using a process that is inspired by biologicalevolution by natural selection [16-20].Genetic programming begins with an initial population of randomlygenerated computer programs, all of which are possible solutionsto a given problem. This step is essentially a random search orsampling of the space of all possible solutions. An example of onetype of computer program, called a binary expression tree, is shownin Figure 1.

Bottom Line: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Program in Human Genetics and Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN, 37232-0700, USA. ritchie@phg.mc.vanderbilt.edu

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.

Show MeSH