1,884 research outputs found
Robust Bayesian Variable Selection for Gene-Environment Interactions
Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++
Interaction Analysis of Repeated Measure Data
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University
RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion
Few-shot relation extraction (FSRE) aims at recognizing unseen relations by
learning with merely a handful of annotated instances. To generalize to new
relations more effectively, this paper proposes a novel pipeline for the FSRE
task based on queRy-information guided Attention and adaptive Prototype fuSion,
namely RAPS. Specifically, RAPS first derives the relation prototype by the
query-information guided attention module, which exploits rich interactive
information between the support instances and the query instances, in order to
obtain more accurate initial prototype representations. Then RAPS elaborately
combines the derived initial prototype with the relation information by the
adaptive prototype fusion mechanism to get the integrated prototype for both
train and prediction. Experiments on the benchmark dataset FewRel 1.0 show a
significant improvement of our method against state-of-the-art methods.Comment: 9 pages, 2 figure
The Bayesian Regularized Quantile Varying Coefficient Model
The quantile varying coefficient (VC) model can flexibly capture dynamical
patterns of regression coefficients. In addition, due to the quantile check
loss function, it is robust against outliers and heavy-tailed distributions of
the response variable, and can provide a more comprehensive picture of modeling
via exploring the conditional quantiles of the response variable. Although
extensive studies have been conducted to examine variable selection for the
high-dimensional quantile varying coefficient models, the Bayesian analysis has
been rarely developed. The Bayesian regularized quantile varying coefficient
model has been proposed to incorporate robustness against data heterogeneity
while accommodating the non-linear interactions between the effect modifier and
predictors. Selecting important varying coefficients can be achieved through
Bayesian variable selection. Incorporating the multivariate spike-and-slab
priors further improves performance by inducing exact sparsity. The Gibbs
sampler has been derived to conduct efficient posterior inference of the sparse
Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit
of the proposed model in selection and estimation accuracy over the
alternatives has been systematically investigated in simulation under specific
quantile levels and multiple heavy-tailed model errors. In the case study, the
proposed model leads to identification of biologically sensible markers in a
non-linear gene-environment interaction study using the NHS data
Microwave assistant synthesis of trans-4-nitrostilbene derivatives in solvent free condition
A general method for the synthesis of trans-4-nitrostilbenes has been developed. The trans-4-nitrostilbene could be synthesized in good yields under microwave irradiation within 10 min through Perkin reaction by using 4-nitrophenylacetic acid, benzaldehydes and pyrrolidine
Fractional partial differential variational inequality
In this present paper, we introduce and study a dynamical systems involving
fractional derivative operator and nonlocal condition, which is constituted of
a fractional evolution equation and a time-dependent variational inequality,
and is named as fractional partial differential variational inequality (FPDVI,
for short). By employing the estimates involving the one-and two-parameter
Mittag-Leffler functions, fixed-point theory for set-value mappings, and
non-compactness measure theory, we develop a general framework to establish the
existence of smooth solutions to (FPDVI).Comment: 12 page
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