Literature DB >> 21212066

Predicting the future of genetic risk prediction.

Nilanjan Chatterjee1, Ju-Hyun Park, Neil Caporaso, Mitchell H Gail.   

Abstract

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Year:  2011        PMID： 21212066      PMCID： PMC5376095          DOI： 10.1158/1055-9965.EPI-10-1022

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN： 1055-9965            Impact factor:   4.254


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  22 in total

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Review 2.  Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.

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3.  Assessing the probability that a positive report is false: an approach for molecular epidemiology studies.

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4.  Performance of common genetic variants in breast-cancer risk models.

Authors:  Sholom Wacholder; Patricia Hartge; Ross Prentice; Montserrat Garcia-Closas; Heather Spencer Feigelson; W Ryan Diver; Michael J Thun; David G Cox; Susan E Hankinson; Peter Kraft; Bernard Rosner; Christine D Berg; Louise A Brinton; Jolanta Lissowska; Mark E Sherman; Rowan Chlebowski; Charles Kooperberg; Rebecca D Jackson; Dennis W Buckman; Peter Hui; Ruth Pfeiffer; Kevin B Jacobs; Gilles D Thomas; Robert N Hoover; Mitchell H Gail; Stephen J Chanock; David J Hunter
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5.  Polygenes, risk prediction, and targeted prevention of breast cancer.

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6.  Decision curve analysis: a novel method for evaluating prediction models.

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7.  An expanded risk prediction model for lung cancer.

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Journal:  Cancer Prev Res (Phila)       Date:  2008-09

8.  Using relative utility curves to evaluate risk prediction.

Authors:  Stuart G Baker; Nancy R Cook; Andrew Vickers; Barnett S Kramer
Journal:  J R Stat Soc Ser A Stat Soc       Date:  2009-10-01       Impact factor: 2.483

9.  Projecting individualized probabilities of developing bladder cancer in white individuals.

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Review 10.  Missing heritability and strategies for finding the underlying causes of complex disease.

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1.  Genetic epidemiology with a capital E: where will we be in another 10 years?

Authors:  Duncan C Thomas
Journal:  Genet Epidemiol       Date:  2012-02-06       Impact factor: 2.135

2.  Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers.

Authors:  Ju-Hyun Park; Mitchell H Gail; Mark H Greene; Nilanjan Chatterjee
Journal:  J Clin Oncol       Date:  2012-05-14       Impact factor: 44.544

3.  A weighted polygenic risk score using 14 known susceptibility variants to estimate risk and age onset of psoriasis in Han Chinese.

Authors:  Xianyong Yin; Hui Cheng; Yan Lin; Nathan E Wineinger; Fusheng Zhou; Yujun Sheng; Chao Yang; Pan Li; Feng Li; Changbing Shen; Sen Yang; Nicholas J Schork; Xuejun Zhang
Journal:  PLoS One       Date:  2015-05-01       Impact factor: 3.240

4.  MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project.

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Journal:  Cancer Manag Res       Date:  2018-05-14       Impact factor: 3.989

5.  Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population.

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Journal:  Breast Cancer Res       Date:  2014-06-18       Impact factor: 6.466

6.  Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure: impact of mole phenotype on model performance.

Authors:  Lauren A Penn; Meng Qian; Enhan Zhang; Elise Ng; Yongzhao Shao; Marianne Berwick; DeAnn Lazovich; David Polsky
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  6 in total

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