Genetic programming neural networks as a bioinformatics tool for human genetics

Marylyn D. Ritchie; Christopher S. Coffey; Jason H. Moore

doi:10.1007/978-3-540-24854-5_44

Genetic programming neural networks as a bioinformatics tool for human genetics

Marylyn D. Ritchie, Christopher S. Coffey, Jason H. Moore

Biochemistry & Molecular Biology

Research output: Chapter in Book/Report/Conference proceeding › Chapter

33 Scopus citations

Abstract

The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Kalyanmoy Deb, Riccardo Poli, Owen Holland, Kalyanmoy Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Dasgupta, Dario Floreano, James Foster, Mark Harman, Pier Luca Lanzi, Lee Spector, Andrea G. B. Tettamanzi, Dirk Thierens, Andrew M. Tyrrell
Publisher	Springer Verlag
Pages	438-448
Number of pages	11
ISBN (Print)	3540223444, 9783540223443
DOIs	https://doi.org/10.1007/978-3-540-24854-5_44
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3102
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-24854-5_44

Cite this

Ritchie, M. D., Coffey, C. S., & Moore, J. H. (2004). Genetic programming neural networks as a bioinformatics tool for human genetics. In K. Deb, R. Poli, O. Holland, K. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, P. L. Lanzi, L. Spector, A. G. B. Tettamanzi, D. Thierens, & A. M. Tyrrell (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 438-448). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3102). Springer Verlag. https://doi.org/10.1007/978-3-540-24854-5_44

Ritchie, Marylyn D. ; Coffey, Christopher S. ; Moore, Jason H. / Genetic programming neural networks as a bioinformatics tool for human genetics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Kalyanmoy Deb ; Riccardo Poli ; Owen Holland ; Kalyanmoy Banzhaf ; Hans-Georg Beyer ; Edmund Burke ; Paul Darwen ; Dipankar Dasgupta ; Dario Floreano ; James Foster ; Mark Harman ; Pier Luca Lanzi ; Lee Spector ; Andrea G. B. Tettamanzi ; Dirk Thierens ; Andrew M. Tyrrell. Springer Verlag, 2004. pp. 438-448 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.",

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Ritchie, MD, Coffey, CS & Moore, JH 2004, Genetic programming neural networks as a bioinformatics tool for human genetics. in K Deb, R Poli, O Holland, K Banzhaf, H-G Beyer, E Burke, P Darwen, D Dasgupta, D Floreano, J Foster, M Harman, PL Lanzi, L Spector, AGB Tettamanzi, D Thierens & AM Tyrrell (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3102, Springer Verlag, pp. 438-448. https://doi.org/10.1007/978-3-540-24854-5_44

Genetic programming neural networks as a bioinformatics tool for human genetics. / Ritchie, Marylyn D.; Coffey, Christopher S.; Moore, Jason H.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Kalyanmoy Deb; Riccardo Poli; Owen Holland; Kalyanmoy Banzhaf; Hans-Georg Beyer; Edmund Burke; Paul Darwen; Dipankar Dasgupta; Dario Floreano; James Foster; Mark Harman; Pier Luca Lanzi; Lee Spector; Andrea G. B. Tettamanzi; Dirk Thierens; Andrew M. Tyrrell. Springer Verlag, 2004. p. 438-448 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3102).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AU - Coffey, Christopher S.

AU - Moore, Jason H.

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Ritchie MD, Coffey CS, Moore JH. Genetic programming neural networks as a bioinformatics tool for human genetics. In Deb K, Poli R, Holland O, Banzhaf K, Beyer HG, Burke E, Darwen P, Dasgupta D, Floreano D, Foster J, Harman M, Lanzi PL, Spector L, Tettamanzi AGB, Thierens D, Tyrrell AM, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 438-448. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24854-5_44

Genetic programming neural networks as a bioinformatics tool for human genetics

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