- author = "Jesus K. Estrada-Gil and Juan C. Fernandez-Lopez and Enrique Hernandez-Lemus and Irma Silva-Zolezzi and Alfredo Hidalgo-Miranda and Gerardo Jimenez-Sanchez and Edgar E. Vallejo-Clemente",
- title = "GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases",
- journal = "Bioinformatics",
- year = "2007",
- volume = "13",
- number = "13",
- pages = "i167--i174",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1460-2059",
-
DOI = "
doi:10.1093/bioinformatics/btm205",
-
abstract = "Motivation: The identification of risk-associated
genetic variants in common diseases remains a challenge
to the biomedical research community. It has been
suggested that common statistical approaches that
exclusively measure main effects are often unable to
detect interactions between some of these variants.
Detecting and interpreting interactions is a
challenging open problem from the statistical and
computational perspectives. Methods in computing
science may improve our understanding on the mechanisms
of genetic disease by detecting interactions even in
the presence of very low heritabilities.
Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors.
Availability: Information on the generated data sets and executable code is available upon request.",
- notes = "PMID: 17646293 [PubMed - in process]",
