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  3. Flexible Bayesian Inference For Identifying Significantly Correlated Multiple Pathway Sets.
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  3. Flexible Bayesian Inference For Identifying Significantly Correlated Multiple Pathway Sets.

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Flexible Bayesian Inference for Identifying Significantly Correlated Multiple Pathway Sets.

PhilGeun Jin1, Youngho Yun1, Inyoung Kim1

  • 1Department of Statistics, Virginia Polytechnic Institute and State University, VA, USA.

Statistics in Medicine
|February 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a flexible Bayesian method to find important gene pathways linked to health outcomes. It addresses complex interactions between pathways for more accurate genetic pathway analysis in diseases like type II diabetes.

Keywords:
Bayes factorfused modelkernel machine regressionmultiple testing

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Pathway-based analysis in genetics is crucial for detecting subtle expression changes.
  • Interactions among biological pathways complicate marginal analysis, leading to potential errors.
  • Existing methods often fail to account for inter-pathway dependencies in clinical outcome studies.

Purpose of the Study:

  • To develop a flexible Bayesian inference method for identifying significantly correlated high-dimensional functions (pathways) with a response variable.
  • To address the challenge of unknown and complex relationships due to inter-pathway dependence.
  • To improve the accuracy of genetic pathway analysis by accounting for pathway interactions.

Main Methods:

  • Proposed a generalized fused kernel machine regression approach.
  • Developed a data-driven, flexible Bayesian inference framework.
  • Utilized Bayes factor for multiple testing adjustment, accommodating dependence through a flexible structure.

    • Main Results:

    • The proposed method effectively identifies significantly correlated high-dimensional functions with continuous or binary response variables.
    • Bayesian inference with Bayes factor adjustment successfully accommodates inter-pathway dependence.
    • Demonstrated benefits through simulation studies and analysis of genetic pathway data for type II diabetes.

    Conclusions:

    • The flexible Bayesian inference method offers a robust approach for analyzing high-dimensional functions in complex biological systems.
    • Accounting for pathway interactions is essential for accurate identification of significant functions and reliable clinical outcome prediction.
    • The method provides a valuable tool for genetic pathway analysis, particularly in complex diseases like type II diabetes.