Bayesian network-response regression.
Abstract
Motivation: There is increasing interest in learning how human brain networks vary
as a function of a continuous trait, but flexible and efficient procedures to accomplish
this goal are limited. We develop a Bayesian semiparametric model, which combines
low-rank factorizations and flexible Gaussian process priors to learn changes in the
conditional expectation of a network-valued random variable across the values of a
continuous predictor, while including subject-specific random effects. Results: The
formulation leads to a general framework for inference on changes in brain network
structures across human traits, facilitating borrowing of information and coherently
characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation
along with simple procedures for inference, prediction and goodness-of-fit assessments.
The model is applied to learn how human brain networks vary across individuals with
different intelligence scores. Results provide interesting insights on the association
between intelligence and brain connectivity, while demonstrating good predictive performance.
Availability and Implementation: Source code implemented in R and data are available
at https://github.com/wangronglu/BNRR. Contact: rl.wang@duke.edu. Supplementary information:
Supplementary data are available at Bioinformatics online.
Type
Journal articlePermalink
https://hdl.handle.net/10161/14605Published Version (Please cite this version)
10.1093/bioinformatics/btx050Publication Info
Wang, Lu; Durante, Daniele; Jung, Rex E; & Dunson, David B (2017). Bayesian network-response regression. Bioinformatics. 10.1093/bioinformatics/btx050. Retrieved from https://hdl.handle.net/10161/14605.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex
data - methods development is directly motivated by challenging applications in ecology/biodiversity,
neuroscience, environmental health, criminal justice/fairness, and more. We seek
to develop new modeling frameworks, algorithms and corresponding code that can be
used routinely by scientists and decision makers. We are also interested in new inference
framework and in studying theoretical properties
Lu Wang
Teaching Assistant
Education
M.S. in Finance (2014), Xiamen University, China
B.S. in Mathematics (2011), Zhejiang University, China
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