Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option
Issue No. 01 - January/February (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.44
J. R. Quevedo , Dept. de Inf., Univ. de Oviedo en Gijon, Gijon, Spain
A. Bahamonde , Centro de Intel. Artificial, Univ. de Oviedo en Gijon, Gijon, Spain
M. Perez-Enciso , Dept. de Cienc. Animal i dels Aliments, Univ. Autonoma de Barcelona, Bellaterra, Spain
O. Luaces , Centro de Intel. Artificial, Univ. de Oviedo en Gijon, Gijon, Spain
Genome-wide association studies (GWA) try to identify the genetic polymorphisms associated with variation in phenotypes. However, the most significant genetic variants may have a small predictive power to forecast the future development of common diseases. We study the prediction of the risk of developing a disease given genome-wide genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but in doubtful situations may reject making a classification. To test the reliability of our proposal, we used the Wellcome Trust Case Control Consortium (WTCCC) data set, comprising 14,000 cases of seven common human diseases and 3,000 shared controls.
Diseases, Bioinformatics, Diabetes, Biological cells, Input variables, Genomics
J. R. Quevedo, A. Bahamonde, M. Perez-Enciso and O. Luaces, "Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 88-97, 2011.