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Semiparametric Bayesian kernel survival model for evaluating pathway effects.

Lin Zhang1, Inyoung Kim1

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

Statistical Methods in Medical Research
|October 6, 2018
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Summary
This summary is machine-generated.

This study introduces a new Bayesian model for analyzing gene pathways and survival data, especially for complex datasets. The method improves the identification of disease-related pathways, aiding drug discovery and medical strategies.

Keywords:
Bayes factorBayesianGaussian processbreast cancergene pathwaykernel machinemultiple comparisonsurvival

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Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • High-dimensional biological data necessitates advanced analytical methods.
  • Pathway-based analysis offers superior detection of subtle gene expression changes compared to gene-based analysis.
  • Identifying disease-regulating gene pathways is crucial for novel therapeutic strategies and drug discovery.

Purpose of the Study:

  • To propose a novel semiparametric Bayesian kernel survival model (s-BKSurv) for analyzing high-dimensional gene pathway data.
  • To investigate the impact of clinical covariates and pathway gene expression on survival outcomes.
  • To address the challenges of "small n, large p" data structures in survival analysis.

Main Methods:

  • Developed a semiparametric Bayesian kernel survival model (s-BKSurv).
  • Utilized a Gaussian kernel machine to model high-dimensional pathway functions and gene interactions.
  • Implemented a similarity-dependent Bayes factor procedure for family-wise error rate control in a Bayesian framework.

Main Results:

  • The s-BKSurv model demonstrated superior performance in simulation studies.
  • The approach effectively analyzed breast cancer gene-pathway data with "small n, large p" characteristics.
  • The proposed method successfully identified relevant gene pathways influencing survival outcomes.

Conclusions:

  • The s-BKSurv model provides a robust framework for pathway-based survival analysis.
  • This approach enhances the identification of disease-associated pathways, supporting precision medicine.
  • The methodology offers a valuable tool for drug discovery and developing targeted therapies.