|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.
Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.
Authors: Zhang L, Baladandayuthapani V, Mallick BK, Manyam GC, Thompson PA, Bondy ML, Do KA
Source: J R Stat Soc Ser C Appl Stat, 2014 Aug;63(4), p. 595-620.
Bayesian sparse graphical models and their mixtures.
Authors: Talluri R, Baladandayuthapani V, Mallick BK
Source: Stat, 2014 Jan 1;3(1), p. 109-125.
Integrative network-based Bayesian analysis of diverse genomics data.
Authors: Wang W, Baladandayuthapani V, Holmes CC, Do KA
Source: BMC Bioinformatics, 2013;14 Suppl 13, p. S8.
EPub date: 2013 Oct 1.
Bayesian methods for expression-based integration of various types of genomics data.
Authors: Jennings EM, Morris JS, Carroll RJ, Manyam GC, Baladandayuthapani V
Source: EURASIP J Bioinform Syst Biol, 2013 Sep 21;2013(1), p. 13.
EPub date: 2013 Sep 21.
Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines.
Authors: Srivastava S, Wang W, Manyam G, Ordonez C, Baladandayuthapani V
Source: EURASIP J Bioinform Syst Biol, 2013 Jun 28;2013(1), p. 9.
EPub date: 2013 Jun 28.
iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.
Authors: Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do KA
Source: Bioinformatics, 2013 Jan 15;29(2), p. 149-59.
EPub date: 2012 Nov 9.