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Detalle de: Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option

Title Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option
Abstract

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.

Apa Reference Quevedo, J.R., Bahamonde, A., Perez-Enciso, M. & Luaces, O. (2012). Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(1), 88-97. 10.1109/TCBB.2011.44
Doi 10.1109/TCBB.2011.44
Publication Date Jan 2012
Publication Type Article
Researcher Miguel Pérez Enciso
Ontology Machine Learning in Biology, Genomics, Population genomics
Research Groups
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