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Likelihood ratios for genome medicine
Patients are beginning to present to healthcare providers with the results of high-throughput individualized genotyping, and interpreting these results in the context of the explosive growth of literature linking individual variants with disease may seem daunting. However, we suggest that results of...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887074/ https://www.ncbi.nlm.nih.gov/pubmed/20497613 http://dx.doi.org/10.1186/gm151 |
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author | Morgan, Alexander A Chen, Rong Butte, Atul J |
author_facet | Morgan, Alexander A Chen, Rong Butte, Atul J |
author_sort | Morgan, Alexander A |
collection | PubMed |
description | Patients are beginning to present to healthcare providers with the results of high-throughput individualized genotyping, and interpreting these results in the context of the explosive growth of literature linking individual variants with disease may seem daunting. However, we suggest that results of a personal genomic analysis may be viewed as a panel of many tests for multiple diseases. By using well-established methods of evidence based medicine, these very many parallel tests may be combined using likelihood ratios to report a post-test probability of disease for use in patient assessment. |
format | Text |
id | pubmed-2887074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28870742011-05-17 Likelihood ratios for genome medicine Morgan, Alexander A Chen, Rong Butte, Atul J Genome Med Commentary Patients are beginning to present to healthcare providers with the results of high-throughput individualized genotyping, and interpreting these results in the context of the explosive growth of literature linking individual variants with disease may seem daunting. However, we suggest that results of a personal genomic analysis may be viewed as a panel of many tests for multiple diseases. By using well-established methods of evidence based medicine, these very many parallel tests may be combined using likelihood ratios to report a post-test probability of disease for use in patient assessment. BioMed Central 2010-05-17 /pmc/articles/PMC2887074/ /pubmed/20497613 http://dx.doi.org/10.1186/gm151 Text en Copyright ©2010 BioMed Central Ltd |
spellingShingle | Commentary Morgan, Alexander A Chen, Rong Butte, Atul J Likelihood ratios for genome medicine |
title | Likelihood ratios for genome medicine |
title_full | Likelihood ratios for genome medicine |
title_fullStr | Likelihood ratios for genome medicine |
title_full_unstemmed | Likelihood ratios for genome medicine |
title_short | Likelihood ratios for genome medicine |
title_sort | likelihood ratios for genome medicine |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887074/ https://www.ncbi.nlm.nih.gov/pubmed/20497613 http://dx.doi.org/10.1186/gm151 |
work_keys_str_mv | AT morganalexandera likelihoodratiosforgenomemedicine AT chenrong likelihoodratiosforgenomemedicine AT butteatulj likelihoodratiosforgenomemedicine |