|Grant Number:||5R01CA160736-03 Interpret this number|
|Primary Investigator:||Baladandayuthapani, Veerabhadran|
|Organization:||University Of Tx Md Anderson Can Ctr|
|Project Title:||Integrative Methods for High-Dimensional Genomics Data|
DESCRIPTION (provided by applicant): The primary objective of this proposal is to develop adaptive and exible statistical models for analyses of multivariate, functional and spatial data from high-throughput biomedical studies. These studies raise computational, modeling, and inferential challenges with respect to high-dimensionality as well as structured dependency induced by the various aspects of the processes generating the data. Our work is motivated by, and will be applied to, data from a variety of high- throughput cancer-related studies that were conducted by our biomedical collaborators, in genomics, epigenomics and transcriptomics; although our methods are generally applicable to other contexts. The short-term objective of this research is to develop novel statistical methods and computational tools for statistical and probabilistic modeling of such high-throughput data with particular emphasis on integrative methods to combine information within and across dierent assays as well as clinical data to answer important biological questions. Our long-term goal is to improve risk prediction and treatment selection in cancer prevention, diagnosis and prognosis. We will accomplish the objective of this application by pursuing the following ve specic aims (1) develop new methodology for Bayesian adaptive generalized functional linear mixed models, allowing for local and nonlinear association structures between scalar responses and functional predictors (2) develop hierarchical Bayesian joint models for integrating diverse types of multivariate and functional data. (3) develop Bayesian spatial-functional process models for spatially indexed high-dimensional functional data, methods for data requiring a broader class of within-function and between-function covariance structures using exible families of covariance functions. (4) develop multivariate Bayesian spatial-functional models for joint modeling of multiple spatially indexed functional data. (5) develop ecient, user-friendly and freely available software for the proposed methods.
Ibag: Integrative Bayesian Analysis Of High-dimensional Multiplatform Genomics Data
Authors: Wang W. , Baladandayuthapani V. , Morris J.S. , Broom B.M. , Manyam G. , Do K.A. .
Source: Bioinformatics (oxford, England), 2013-01-15 00:00:00.0; 29(2), p. 149-59.
Integrating Multi-platform Genomic Data Using Hierarchical Bayesian Relevance Vector Machines
Authors: Srivastava S. , Wang W. , Manyam G. , Ordonez C. , Baladandayuthapani V. .
Source: Eurasip Journal On Bioinformatics & Systems Biology, 2013; 2013(1), p. 9.
Bayesian Methods For Expression-based Integration Of Various Types Of Genomics Data
Authors: Jennings E.M. , Morris J.S. , Carroll R.J. , Manyam G.C. , Baladandayuthapani V. .
Source: Eurasip Journal On Bioinformatics & Systems Biology, 2013; 2013(1), p. 13.
Integrative Network-based Bayesian Analysis Of Diverse Genomics Data
Authors: Wang W. , Baladandayuthapani V. , Holmes C.C. , Do K.A. .
Source: Bmc Bioinformatics, 2013; 14 Suppl 13, p. S8.
Bayesian Sparse Graphical Models And Their Mixtures
Authors: Talluri R. , Baladandayuthapani V. , Mallick B.K. .
Source: Stat, 2014-01-01 00:00:00.0; 3(1), p. 109-125.
Bayesian Hierarchical Structured Variable Selection Methods With Application To Mip Studies In Breast Cancer
Authors: Zhang L. , Baladandayuthapani V. , Mallick B.K. , Manyam G.C. , Thompson P.A. , Bondy M.L. , Do K.A. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2014 Aug; 63(4), p. 595-620.
Bayesian Function-on-function Regression For Multilevel Functional Data
Authors: Meyer M.J. , Coull B.A. , Versace F. , Cinciripini P. , Morris J.S. .
Source: Biometrics, 2015 Sep; 71(3), p. 563-74.
Bayesian Nonlinear Model Selection For Gene Regulatory Networks
Authors: Ni Y. , Stingo F.C. , Baladandayuthapani V. .
Source: Biometrics, 2015 Sep; 71(3), p. 585-95.
Latent Feature Decompositions For Integrative Analysis Of Multi-platform Genomic Data
Authors: Gregory K.B. , Momin A.A. , Coombes K.R. , Baladandayuthapani V. .
Source: Ieee/acm Transactions On Computational Biology And Bioinformatics / Ieee, Acm, 2014 Nov-Dec; 11(6), p. 984-94.
Dingo: Differential Network Analysis In Genomics
Authors: Ha M.J. , Baladandayuthapani V. , Do K.A. .
Source: Bioinformatics (oxford, England), 2015-11-01 00:00:00.0; 31(21), p. 3413-20.
A Two-sample Test For Equality Of Means In High Dimension
Authors: Gregory K.B. , Carroll R.J. , Baladandayuthapani V. , Lahiri S.N. .
Source: Journal Of The American Statistical Association, 2015-06-01 00:00:00.0; 110(510), p. 837-849.
Integrative Bayesian Analysis Of Neuroimaging-genetic Data With Application To Cocaine Dependence
Authors: Azadeh S. , Hobbs B.P. , Ma L. , Nielsen D.A. , Moeller F.G. , Baladandayuthapani V. .
Source: Neuroimage, 2016-01-15 00:00:00.0; 125, p. 813-24.
Demarcate: Density-based Magnetic Resonance Image Clustering For Assessing Tumor Heterogeneity In Cancer
Authors: Saha A. , Banerjee S. , Kurtek S. , Narang S. , Lee J. , Rao G. , Martinez J. , Bharath K. , Rao A.U. , Baladandayuthapani V. .
Source: Neuroimage. Clinical, 2016; 12, p. 132-43.