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New Genetic Variants Improve Personalized Breast Cancer Diagnosis
Recent large-scale genome-wide association studies (GWAS) have identified a number of new genetic variants associated with breast cancer. However, the degree to which these genetic variants improve breast cancer diagnosis in concert with mammography remains unknown. We conducted a case-control study...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333695/ https://www.ncbi.nlm.nih.gov/pubmed/25717406 |
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author | Liu, Jie Page, David Peissig, Peggy McCarty, Catherine Onitilo, Adedayo A. Trentham-Dietz, Amy Burnside, Elizabeth |
author_facet | Liu, Jie Page, David Peissig, Peggy McCarty, Catherine Onitilo, Adedayo A. Trentham-Dietz, Amy Burnside, Elizabeth |
author_sort | Liu, Jie |
collection | PubMed |
description | Recent large-scale genome-wide association studies (GWAS) have identified a number of new genetic variants associated with breast cancer. However, the degree to which these genetic variants improve breast cancer diagnosis in concert with mammography remains unknown. We conducted a case-control study and collected mammography features and 77 genetic variants which reflect the state of the art GWAS findings on breast cancer. A naïve Bayes model was developed on the mammography features and these genetic variants. We observed that the incorporation of the genetic variants significantly improved breast cancer diagnosis based on mammographic findings. |
format | Online Article Text |
id | pubmed-4333695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-43336952015-02-25 New Genetic Variants Improve Personalized Breast Cancer Diagnosis Liu, Jie Page, David Peissig, Peggy McCarty, Catherine Onitilo, Adedayo A. Trentham-Dietz, Amy Burnside, Elizabeth AMIA Jt Summits Transl Sci Proc Articles Recent large-scale genome-wide association studies (GWAS) have identified a number of new genetic variants associated with breast cancer. However, the degree to which these genetic variants improve breast cancer diagnosis in concert with mammography remains unknown. We conducted a case-control study and collected mammography features and 77 genetic variants which reflect the state of the art GWAS findings on breast cancer. A naïve Bayes model was developed on the mammography features and these genetic variants. We observed that the incorporation of the genetic variants significantly improved breast cancer diagnosis based on mammographic findings. American Medical Informatics Association 2014-04-07 /pmc/articles/PMC4333695/ /pubmed/25717406 Text en ©2014 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose |
spellingShingle | Articles Liu, Jie Page, David Peissig, Peggy McCarty, Catherine Onitilo, Adedayo A. Trentham-Dietz, Amy Burnside, Elizabeth New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title | New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title_full | New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title_fullStr | New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title_full_unstemmed | New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title_short | New Genetic Variants Improve Personalized Breast Cancer Diagnosis |
title_sort | new genetic variants improve personalized breast cancer diagnosis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333695/ https://www.ncbi.nlm.nih.gov/pubmed/25717406 |
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