Skip to main content
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
Back to top


? 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.

Back to top


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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

Authors: Rossell D. .
Source: Metode Science Studies Journal : Annual Review, 2015; 5, p. 143-149.
PMID: 27722040
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

Authors: Johnson V.E. .
Source: Annals Of Statistics, 2013; 41(4), p. 1716-1741.
PMID: 24659829
Related Citations

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
Related Citations

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
Related Citations

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
Related Citations

Back to Top