Symbolic Discriminant Analysis of Microarray Data in Automimmune Disease
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- @Article{moore:2002:SDA,
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author = "Jason H. Moore and Joel S. Parker and
Nancy J. Olsen and Thomas M. Aune",
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title = "Symbolic Discriminant Analysis of Microarray Data in
Automimmune Disease",
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journal = "Genetic Epidemiology",
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year = "2002",
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volume = "23",
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pages = "57--69",
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keywords = "genetic algorithms, genetic programming, DNA chip,
rheumatoid arthritis, systemic lupus erythematosus, flu
vaccine",
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DOI = "doi:10.1002/gepi.1117",
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abstract = "New laboratory technologies such as DNA microarrays
have made it possible to measure the expression levels
of thousands of genes simultaneously in a particular
cell or tissue. The challenge for genetic
epidemiologists will be to develop statistical and
computational methods that are able to identify subsets
of gene expression variables that classify and predict
clinical endpoints. Linear discriminant analysis is a
popular multivariate statistical approach for
classification of observations into groups. This is
because the theory is well described and the method is
easy to implement and interpret. However, an important
limitation is that linear discriminant functions need
to be prespecified. To address this limitation and the
limitation of linearity, we have developed symbolic
discriminant analysis (SDA) for the automatic selection
of gene expression variables and discriminant functions
that can take any form. In the present study, we
demonstrate that SDA is capable of identifying
combinations of gene expression variables that are able
to classify and predict autoimmune diseases",
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notes = "LilGP, PVM, LOOCV, 110 node beowulf, Linux",
- }
Genetic Programming entries for
Jason H Moore
Joel S Parker
Nancy J Olsen
Thomas M Aune
Citations