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Grant Details

Grant Number: 5R01CA158113-07 Interpret this number
Primary Investigator: Johnson, Valen
Organization: Texas A&M University
Project Title: Consistent Variable Selection in P>>n Settings
Fiscal Year: 2018
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Abstract

? DESCRIPTION (provided by applicant): Molecular signature-guided clinical therapies are critical to advancing the treatment of cancer, and there has been a recent explosion in the number of types of molecular data that can potentially be used to identify mutations, expression levels, and methylations (and combinations of these effects) that contribute to cancer gene functioning. Vast stores of such data are now publicly available in repositories, like The Cancer Genome Atlas Projects and the International Cancer Genome Consortium, where they await statistical analyses. Like finding a needle in a haystack, the central problem that arises in the analyses of these data is the problem of identifying important prognostic factors from huge numbers of non-prognostic factors. The investigators of this project have recently developed a new method that can accomplish this feat. Their approach has proven to correctly identify important factors that predict outcomes when there are many more factors that can be used for prediction than there are observations of an outcome, and recent theoretical developments and simulation studies have demonstrated that these results can be extended to situations in which there are many, many more possible gene expression values than there are tissue samples from cancer patients. The goal of this project is to extend these methods so that they can be applied to broader classes of patient outcome data, to make these methods more computationally efficient so that they can be applied routinely to massive genomic datasets, to apply these methods to existing cancer studies, and to incorporate these new methods into software tools that can be distributed to cancer researchers throughout the world so that they can more effectively identify genetic mutations that are either associated with cancer functioning or predictive of the success of new or existing cancer therapies.

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Publications

Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings.
Authors: Shin M. , Bhattacharya A. , Johnson V.E. .
Source: Statistica Sinica, 2018 Apr; 28(2), p. 1053-1078.
PMID: 29643721
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Bayesian block-diagonal variable selection and model averaging.
Authors: Papaspiliopoulos O. , Rossell D. .
Source: Biometrika, 2017 Jun; 104(2), p. 343-359.
EPub date: 2017-04-24 00:00:00.0.
PMID: 29861501
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On the Reproducibility of Psychological Science.
Authors: Johnson V.E. , Payne R.D. , Wang T. , Asher A. , Mandal S. .
Source: Journal Of The American Statistical Association, 2017; 112(517), p. 1-10.
EPub date: 2016-10-07 00:00:00.0.
PMID: 29861517
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NON-LOCAL PRIORS FOR HIGH-DIMENSIONAL ESTIMATION.
Authors: Rossell D. , Telesca D. .
Source: Journal Of The American Statistical Association, 2017; 112(517), p. 254-265.
EPub date: 2017-05-03 00:00:00.0.
PMID: 29881129
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Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.
Authors: Nikooienejad A. , Wang W. , Johnson V.E. .
Source: Bioinformatics (oxford, England), 2016-05-01 00:00:00.0; 32(9), p. 1338-45.
EPub date: 2016-05-01 00:00:00.0.
PMID: 26740524
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A robust Bayesian dose-finding design for phase I/II clinical trials.
Authors: Liu S. , Johnson V.E. .
Source: Biostatistics (oxford, England), 2016 Apr; 17(2), p. 249-63.
PMID: 26486139
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Designing alternative splicing RNA-seq studies. Beyond generic guidelines.
Authors: Stephan-Otto Attolini C. , Peña V. , Rossell D. .
Source: Bioinformatics (oxford, England), 2015-11-15 00:00:00.0; 31(22), p. 3631-7.
EPub date: 2015-11-15 00:00:00.0.
PMID: 26220961
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Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging.
Authors: Wang Y. , Hobbs B.P. , Hu J. , Ng C.S. , Do K.A. .
Source: Biometrics, 2015 Sep; 71(3), p. 792-802.
PMID: 25851056
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A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics.
Authors: Hu J. , Zhu H. , Hu F. .
Source: Journal Of The American Statistical Association, 2015-04-22 00:00:00.0; 110(509), p. 357-367.
PMID: 26120220
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Detecting differential patterns of interaction in molecular pathways.
Authors: Yajima M. , Telesca D. , Ji Y. , Müller P. .
Source: Biostatistics (oxford, England), 2015 Apr; 16(2), p. 240-51.
PMID: 25519431
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Estimating and Identifying Unspecified Correlation Structure for Longitudinal Data.
Authors: Hu J. , Wang P. , Qu A. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2015-04-01 00:00:00.0; 24(2), p. 455-476.
PMID: 26361433
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A K-fold Averaging Cross-validation Procedure.
Authors: Jung Y. , Hu J. .
Source: Journal Of Nonparametric Statistics, 2015; 27(2), p. 167-179.
PMID: 27630515
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BIG DATA AND STATISTICS: A STATISTICIAN'S PERSPECTIVE.
Authors: Rossell D. .
Source: Metode Science Studies Journal : Annual Review, 2015; 5, p. 143-149.
PMID: 27722040
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Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA.
Authors: Jung Y. , Huang J.Z. , Hu J. .
Source: Journal Of The American Statistical Association, 2014-12-01 00:00:00.0; 109(508), p. 1355-1367.
PMID: 25642005
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Evaluation of image registration spatial accuracy using a Bayesian hierarchical model.
Authors: Liu S. , Yuan Y. , Castillo R. , Guerrero T. , Johnson V.E. .
Source: Biometrics, 2014 Jun; 70(2), p. 366-77.
PMID: 24575781
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QUANTIFYING ALTERNATIVE SPLICING FROM PAIRED-END RNA-SEQUENCING DATA.
Authors: Rossell D. , Stephan-Otto Attolini C. , Kroiss M. , Stöcker A. .
Source: The Annals Of Applied Statistics, 2014 Mar; 8(1), p. 309-330.
PMID: 24795787
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On Numerical Aspects of Bayesian Model Selection in High and Ultrahigh-dimensional Settings.
Authors: Johnson V.E. .
Source: Bayesian Analysis, 2013-12-01 00:00:00.0; 8(4), p. 741-758.
PMID: 24683431
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Revised standards for statistical evidence.
Authors: Johnson V.E. .
Source: Proceedings Of The National Academy Of Sciences Of The United States Of America, 2013-11-26 00:00:00.0; 110(48), p. 19313-7.
EPub date: 2013-11-26 00:00:00.0.
PMID: 24218581
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Bayesian adaptive phase II screening design for combination trials.
Authors: Cai C. , Yuan Y. , Johnson V.E. .
Source: Clinical Trials (london, England), 2013; 10(3), p. 353-62.
PMID: 23359875
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UNIFORMLY MOST POWERFUL BAYESIAN TESTS.
Authors: Johnson V.E. .
Source: Annals Of Statistics, 2013; 41(4), p. 1716-1741.
PMID: 24659829
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Reno: regularized non-parametric analysis of protein lysate array data.
Authors: Li B. , Liang F. , Hu J. , He A.X. .
Source: Bioinformatics (oxford, England), 2012-05-01 00:00:00.0; 28(9), p. 1223-9.
EPub date: 2012-05-01 00:00:00.0.
PMID: 22467912
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Goodness-of-fit diagnostics for Bayesian hierarchical models.
Authors: Yuan Y. , Johnson V.E. .
Source: Biometrics, 2012 Mar; 68(1), p. 156-64.
PMID: 22050079
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Bayesian Model Selection in High-Dimensional Settings.
Authors: Johnson V.E. , Rossell D. .
Source: Journal Of The American Statistical Association, 2012; 107(498), .
PMID: 24363474
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