Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate

Fang, Zaili; Kim, In-Young; Jung, Jeesun

doi:10.1007/s13253-017-0317-2

Cited by 1 publication

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our goal was to identify multiple pathways that affect the glucose level related to diabetes, after adjusting for the BMI effect. We then compared our results with those of the single pathway-based analysis [13] that were applied to the same dataset.…”

Section: Application: Type II Diabetes Genomics Pathway Datamentioning

confidence: 99%

“…We also compared the results of our multi‐pathway‐based analysis to the single pathway‐based method [13]. Among the

20

pathways with the most significant overall pathway effects, we discovered that the significant pathway effects detected by both methods are

\left\{\mathrm{8,101,103,133,144,151,158,172,228,229,230,236,271}\right\}

.…”

Section: Application: Type II Diabetes Genomics Pathway Datamentioning

confidence: 99%

“…Kim et al [18,19] considered a Bayes-factor-based method and a resampling-based method to identify significant sets. Fang et al [13,14] further developed a kernel machine-based approach for evaluating interactions between pathways and covariates. Fang et al [14] also developed a nonparametric variable selection procedure for single-set analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

Lin,

Kim

2024

Statistical Analysis

Self Cite

View full text Add to dashboard Cite

Analyzing correlated high‐dimensional data is a challenging problem in genomics, proteomics, and other related areas. For example, it is important to identify significant genetic pathway effects associated with biomarkers in which a gene pathway is a set of genes that functionally works together to regulate a certain biological process. A pathway‐based analysis can detect a subtle change in expression level that cannot be found using a gene‐based analysis. Here, we refer to pathway as a set and gene as an element in a set. However, it is challenging to select automatically which pathways are highly associated to the outcome when there are multiple pathways. In this paper, we propose a semiparametric multikernel regression model to study the effects of fixed covariates (e.g., clinical variables) and sets of elements (e.g., pathways of genes) to address a problem of detecting signal sets associated to biomarkers. We model the unknown high‐dimension functions of multi‐sets via multiple Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, our variable set selection can be considered a Gaussian process set selection. We develop our Gaussian process set selection under the Bayesian variance component‐selection framework. We incorporate prior knowledge for structural sets by imposing an Ising prior on the model. Our approach can be easily applied in high‐dimensional spaces where the sample size is smaller than the number of variables. An efficient variational Bayes algorithm is developed. We demonstrate the advantages of our approach through simulation studies and through a type II diabetes genetic‐pathway analysis.

show abstract

Section: Application: Type II Diabetes Genomics Pathway Datamentioning

confidence: 99%

“…We also compared the results of our multi‐pathway‐based analysis to the single pathway‐based method [13]. Among the

20

pathways with the most significant overall pathway effects, we discovered that the significant pathway effects detected by both methods are

\left\{\mathrm{8,101,103,133,144,151,158,172,228,229,230,236,271}\right\}

.…”

Section: Application: Type II Diabetes Genomics Pathway Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

Lin,

Kim

2024

Statistical Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate

Cited by 1 publication

References 29 publications

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

Contact Info

Product

Resources

About