Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/cibcb/MotsingerRFDR07,
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author = "Alison A. Motsinger and David M. Reif and
Theresa J. Fanelli and Anna C. Davis and Marylyn D. Ritchie",
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title = "Linkage Disequilibrium in Genetic Association Studies
Improves the Performance of Grammatical Evolution
Neural Networks",
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booktitle = "IEEE Symposium on Computational Intelligence and
Bioinformatics and Computational Biology, CIBCB '07",
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year = "2007",
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pages = "1--8",
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address = "Honolulu, HI, USA",
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month = "1-5 " # apr,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, gene-gene interactions, genetic association
studies, genetic epidemiology, grammatical evolution
neural networks, linkage disequilibrium, biology
computing, diseases, genetics, neural nets",
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ISBN = "1-4244-0710-9",
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URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04221197",
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size = "8 pages",
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abstract = "One of the most important goals in genetic
epidemiology is the identification of genetic
factors/features that predict complex diseases. The
ubiquitous nature of gene-gene interactions in the
underlying etiology of common diseases creates an
important analytical challenge, spurring the
introduction of novel, computational approaches. One
such method is a grammatical evolution neural network
(GENN) approach. GENN has been shown to have high power
to detect such interactions in simulation studies, but
previous studies have ignored an important feature of
most genetic data: linkage disequilibrium (LD). LD
describes the non-random association of alleles not
necessarily on the same chromosome. This results in
strong correlation between variables in a dataset,
which can complicate analysis. In the current study,
data simulations with a range of LD patterns are used
to assess the impact of such correlated variables on
the performance of GENN. Our results show that not only
do patterns of strong LD not decrease the power of GENN
to detect genetic associations, they actually increase
its power",
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notes = "SNP",
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bibdate = "2009-04-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cibcb/cibcb2007.html#MotsingerRFDR07",
- }
Genetic Programming entries for
Alison A Motsinger
David M Reif
Theresa J Fanelli
Anna C Davis
Marylyn D Ritchie
Citations