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  3. Functional Integrative Bayesian Analysis Of High-dimensional Multiplatform Clinicogenomic Data.
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  3. Functional Integrative Bayesian Analysis Of High-dimensional Multiplatform Clinicogenomic Data.

Related Experiment Video

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Rupam Bhattacharyya1,2, Nicholas C Henderson3, Veerabhadran Baladandayuthapani3

  • 1Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48105.

Journal of the American Statistical Association
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed fiBAG, a new framework for analyzing multi-omic data to find disease biomarkers. This method integrates functional genomic data to improve the detection of crucial cellular mechanisms linked to patient survival.

Keywords:
Bayesian variable selectionCancer clinicogenomicsGaussian processesmulti-omic data integrationproteogenomic analyses

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

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Area of Science:

  • Genomics
  • Proteomics
  • Computational Biology

Background:

  • Multi-platform molecular and genomics data offer opportunities for disease understanding and treatment.
  • Current multi-omic integration methods need enhanced approaches for detailed cellular function evaluation.
  • Identifying precise cellular functions is crucial for understanding complex disease mechanisms.

Purpose of the Study:

  • To propose a novel framework, fiBAG, for the simultaneous identification of upstream functional evidence of proteogenomic biomarkers.
  • To incorporate functional knowledge into Bayesian variable selection models for improved signal detection.
  • To enhance the understanding of cellular functions governing complex disease mechanisms.

Main Methods:

  • Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG) framework.
  • Utilizing Gaussian process models to quantify functional evidence via Bayes factors.
  • Mapping Bayes factors to a calibrated spike-and-slab prior for guided variable selection.

    • Main Results:

    • Simulations show integrative methods with functional calibration possess higher power for detecting disease-related markers.
    • fiBAG demonstrates profitability in a pan-cancer analysis across 14 cancer types.
    • Identified and assessed cellular mechanisms of proteogenomic markers associated with cancer stemness and patient survival.

    Conclusions:

    • fiBAG provides a data-driven approach to evaluate cellular functions and improve biomarker discovery.
    • Integrating functional evidence enhances the detection of biologically relevant markers for patient outcomes.
    • The framework offers a powerful tool for proteogenomic biomarker assessment in cancer research.