Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCBB.2011.44 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
polymorphism,diabetes,genome wide association study,genome wide association,bioinformatics,genetics,genomics | Disease,Genotyping,Predictive power,Computer science,Liability,Genome-wide association study,Genomics,Genetic association,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 1 | 1545-5963 |
Citations | PageRank | References |
3 | 0.39 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Ramón Quevedo | 1 | 175 | 15.37 |
| Antonio Bahamonde | 2 | 335 | 31.96 |
| Miguel Pérez-Enciso | 3 | 3 | 0.39 |
| Oscar Luaces | 4 | 281 | 24.59 |