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Items: 1 to 20 of 95

1.

Using the optimal robust receiver operating characteristic (ROC) curve for predictive genetic tests.

Lu Q, Obuchowski N, Won S, Zhu X, Elston RC.

Biometrics. 2010 Jun;66(2):586-93. doi: 10.1111/j.1541-0420.2009.01278.x.

2.

Bagging optimal ROC curve method for predictive genetic tests, with an application for rheumatoid arthritis.

Lu Q, Cui Y, Ye C, Wei C, Elston RC.

J Biopharm Stat. 2010 Mar;20(2):401-14. doi: 10.1080/10543400903572811.

3.
4.

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, Simón-Sánchez J, Nalls MA, Keller M, Hernandez DG, Gibbs JR, Lesage S, Brice A, Heutink P, Martinez M, Wood NW, Hardy J, Singleton AB, Zell A, Gasser T, Sharma M; International Parkinson’s Disease Genomics Consortium..

Hum Mutat. 2012 Dec;33(12):1708-18. doi: 10.1002/humu.22161.

PMID:
22777693
5.

A non-parametric method for building predictive genetic tests on high-dimensional data.

Ye C, Cui Y, Wei C, Elston RC, Zhu J, Lu Q.

Hum Hered. 2011;71(3):161-70. doi: 10.1159/000327299.

6.

Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers.

Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE.

PLoS Genet. 2009 Feb;5(2):e1000337. doi: 10.1371/journal.pgen.1000337.

7.

Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of Type 2 diabetes and related traits in Indians.

Janipalli CS, Kumar MV, Vinay DG, Sandeep MN, Bhaskar S, Kulkarni SR, Aruna M, Joglekar CV, Priyadharshini S, Maheshwari N, Yajnik CS, Chandak GR.

Diabet Med. 2012 Jan;29(1):121-7. doi: 10.1111/j.1464-5491.2011.03438.x.

PMID:
21913964
8.

Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model.

Li H, Yang L, Zhao X, Wang J, Qian J, Chen H, Fan W, Liu H, Jin L, Wang W, Lu D.

BMC Med Genet. 2012 Dec 10;13:118. doi: 10.1186/1471-2350-13-118.

9.

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.

Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, Coin L, Levin M.

PLoS One. 2009 Nov 30;4(11):e8068. doi: 10.1371/journal.pone.0008068.

10.

A comparison of genomic profiles of complex diseases under different models.

Potenciano V, Abad-Grau MM, Alcina A, Matesanz F.

BMC Med Genomics. 2016 Jan 19;9:3. doi: 10.1186/s12920-015-0157-2.

11.

Genetic and clinical risk prediction model for postoperative atrial fibrillation.

Kolek MJ, Muehlschlegel JD, Bush WS, Parvez B, Murray KT, Stein CM, Shoemaker MB, Blair MA, Kor KC, Roden DM, Donahue BS, Fox AA, Shernan SK, Collard CD, Body SC, Darbar D.

Circ Arrhythm Electrophysiol. 2015 Feb;8(1):25-31. doi: 10.1161/CIRCEP.114.002300.

12.

[Genome-wide association study based risk prediction model in predicting lung cancer risk in Chinese].

Zhu M, Cheng Y, Dai J, Xie L, Jin G, Ma H, Hu Z, Shi Y, Lin D, Shen H.

Zhonghua Liu Xing Bing Xue Za Zhi. 2015 Oct;36(10):1047-52. Chinese.

PMID:
26837341
13.

Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information.

Mealiffe ME, Stokowski RP, Rhees BK, Prentice RL, Pettinger M, Hinds DA.

J Natl Cancer Inst. 2010 Nov 3;102(21):1618-27. doi: 10.1093/jnci/djq388.

14.

The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort.

Shigemizu D, Abe T, Morizono T, Johnson TA, Boroevich KA, Hirakawa Y, Ninomiya T, Kiyohara Y, Kubo M, Nakamura Y, Maeda S, Tsunoda T.

PLoS One. 2014 Mar 20;9(3):e92549. doi: 10.1371/journal.pone.0092549.

15.

Strategies for developing prediction models from genome-wide association studies.

Wu J, Pfeiffer RM, Gail MH.

Genet Epidemiol. 2013 Dec;37(8):768-77. doi: 10.1002/gepi.21762.

PMID:
24166696
16.

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

Wacholder S, Hartge P, Prentice R, Garcia-Closas M, Feigelson HS, Diver WR, Thun MJ, Cox DG, Hankinson SE, Kraft P, Rosner B, Berg CD, Brinton LA, Lissowska J, Sherman ME, Chlebowski R, Kooperberg C, Jackson RD, Buckman DW, Hui P, Pfeiffer R, Jacobs KB, Thomas GD, Hoover RN, Gail MH, Chanock SJ, Hunter DJ.

N Engl J Med. 2010 Mar 18;362(11):986-93. doi: 10.1056/NEJMoa0907727. Erratum in: N Engl J Med. 2010 Dec 2;363(23):2272.

17.

Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes.

Winkler C, Krumsiek J, Buettner F, Angermüller C, Giannopoulou EZ, Theis FJ, Ziegler AG, Bonifacio E.

Diabetologia. 2014 Dec;57(12):2521-9. doi: 10.1007/s00125-014-3362-1. Erratum in: Diabetologia. 2015 Jan;58(1):206.

PMID:
25186292
18.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review.

Bao W, Hu FB, Rong S, Rong Y, Bowers K, Schisterman EF, Liu L, Zhang C.

Am J Epidemiol. 2013 Oct 15;178(8):1197-207. doi: 10.1093/aje/kwt123. Review.

19.

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

Hung CF, Breen G, Czamara D, Corre T, Wolf C, Kloiber S, Bergmann S, Craddock N, Gill M, Holsboer F, Jones L, Jones I, Korszun A, Kutalik Z, Lucae S, Maier W, Mors O, Owen MJ, Rice J, Rietschel M, Uher R, Vollenweider P, Waeber G, Craig IW, Farmer AE, Lewis CM, Müller-Myhsok B, Preisig M, McGuffin P, Rivera M.

BMC Med. 2015 Apr 17;13:86. doi: 10.1186/s12916-015-0334-3.

20.

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.

So HC, Sham PC.

PLoS Genet. 2010 Dec 2;6(12):e1001230. doi: 10.1371/journal.pgen.1001230.

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