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Richard E Neapolitan

Showing results (1-10 of 10) with videos related to

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Plos One|February 28, 2015
Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survivalRichard E Neapolitan, Xia Jiang
Plos One|October 17, 2012
Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionalityXia Jiang, Richard E Neapolitan
Genetic Epidemiology|February 14, 2015
LEAP: biomarker inference through learning and evaluating association patternsXia Jiang, Richard E Neapolitan
Briefings in Bioinformatics|March 20, 2015
Evaluation of a two-stage framework for prediction using big genomic dataXia Jiang, Richard E Neapolitan
Journal of the American Medical Informatics Association : JAMIA|April 27, 2017
Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testingScott Morris, Mike Bass, Mirinae Lee, et al.
BMC Bioinformatics|April 2, 2011
Learning genetic epistasis using Bayesian network scoring criteriaXia Jiang, Richard E Neapolitan, M Michael Barmada, et al.
International Journal of Testing|September 28, 2020
Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform InformationScott B Morris, Michael Bass, Elizabeth Howard, et al.
Journal of the American Medical Informatics Association : JAMIA|April 17, 2014
A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasetsXia Jiang, Binghuang Cai, Diyang Xue, et al.
AMIA ... Annual Symposium Proceedings. AMIA Symposium|February 25, 2011
A fast algorithm for learning epistatic genomic relationshipsXia Jiang, Richard E Neapolitan, M Michael Barmada, et al.
BMC Bioinformatics|July 12, 2020
Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasisXia Jiang, Alan Wells, Adam Brufsky, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Plos One|February 28, 2015
Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survivalRichard E Neapolitan, Xia Jiang
Plos One|October 17, 2012
Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionalityXia Jiang, Richard E Neapolitan
Genetic Epidemiology|February 14, 2015
LEAP: biomarker inference through learning and evaluating association patternsXia Jiang, Richard E Neapolitan
Briefings in Bioinformatics|March 20, 2015
Evaluation of a two-stage framework for prediction using big genomic dataXia Jiang, Richard E Neapolitan
Journal of the American Medical Informatics Association : JAMIA|April 27, 2017
Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testingScott Morris, Mike Bass, Mirinae Lee, et al.
BMC Bioinformatics|April 2, 2011
Learning genetic epistasis using Bayesian network scoring criteriaXia Jiang, Richard E Neapolitan, M Michael Barmada, et al.
International Journal of Testing|September 28, 2020
Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform InformationScott B Morris, Michael Bass, Elizabeth Howard, et al.
Journal of the American Medical Informatics Association : JAMIA|April 17, 2014
A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasetsXia Jiang, Binghuang Cai, Diyang Xue, et al.
AMIA ... Annual Symposium Proceedings. AMIA Symposium|February 25, 2011
A fast algorithm for learning epistatic genomic relationshipsXia Jiang, Richard E Neapolitan, M Michael Barmada, et al.
BMC Bioinformatics|July 12, 2020
Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasisXia Jiang, Alan Wells, Adam Brufsky, et al.
Pageof 1