- author = "Alison A Motsinger-Reif and Marylyn D Ritchie",
- title = "Neural networks for genetic epidemiology: past, present, and future",
- journal = "BioData Mining",
- year = "2008",
- volume = "1",
- number = "3",
- month = jul # " 17",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN",
-
DOI = "
doi:10.1186/1756-0381-1-3",
-
abstract = "During the past two decades, the field of human
genetics has experienced an information explosion. The
completion of the human genome project and the
development of high throughput SNP technologies have
created a wealth of data; however, the analysis and
interpretation of these data have created a research
bottleneck. While technology facilitates the
measurement of hundreds or thousands of genes,
statistical and computational methodologies are lacking
for the analysis of these data. New statistical methods
and variable selection strategies must be explored for
identifying disease susceptibility genes for common,
complex diseases. Neural networks (NN) are a class of
pattern recognition methods that have been successfully
implemented for data mining and prediction in a variety
of fields. The application of NN for statistical
genetics studies is an active area of research. Neural
networks have been applied in both linkage and
association analysis for the identification of disease
susceptibility genes.
In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN) and Grammatical Evolution Neural Networks (GENN), for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work.",
- notes = "PMID:",
