TY - JOUR
T1 - Disease liability prediction from large scale genotyping data using classifiers with a reject option
AU - Quevedo, José R.
AU - Bahamonde, Antonio
AU - Pérez-Enciso, Miguel
AU - Luaces, Oscar
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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. © 2012 IEEE.
AB - 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. © 2012 IEEE.
KW - classification with a reject option
KW - Genome-wide analysis
KW - risk of common human diseases
UR - https://www.scopus.com/pages/publications/81455132704
U2 - 10.1109/TCBB.2011.44
DO - 10.1109/TCBB.2011.44
M3 - Article
SN - 1545-5963
VL - 9
SP - 88
EP - 97
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 1
M1 - 5728945
ER -
