ArticleJournal of biopharmaceutical statistics2010
Bagging optimal ROC curve method for predictive genetic tests, with an application for rheumatoid arthritis.
Article in Journal of biopharmaceutical statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Article
- A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins.BMC bioinformatics · 2012Article
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Authors and funding
5 authors.
Funding
Abstract
Translation studies have been initiated to assess the combined effect of genetic loci from recently accomplished genome-wide association studies and the existing risk factors for early disease prediction. We propose a bagging optimal receiver operating characteristic (ROC) curve method to facilitate this research. Through simulation and real data application, we compared the new method with the commonly used allele counting method and logistic regression, and found that the new method yields a better performance. The new method was applied on the Wellcome Trust data set to form a predictive genetic test for rheumatoid arthritis. The formed test reached an area under the curve (AUC) value of 0.7.
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Registered trials
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