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Bayesian predictive modeling of multi-source multi-way data.

Jonathan Kim1, Brian J Sandri2,3, Raghavendra B Rao2,3

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA.

Computational Statistics & Data Analysis
|June 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method to predict outcomes using multi-source molecular data. The approach effectively identifies predictors for early-life iron deficiency (ID) in rhesus monkeys, improving classification accuracy.

Keywords:
Bayesian modelingiron deficiencymulti-omics integrationmulti-way datareduced rank regressiontensors

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Predicting health outcomes often involves complex, multi-source data.
  • Integrating diverse 'omics data across developmental time points presents analytical challenges.

Purpose of the Study:

  • To develop a Bayesian statistical framework for predicting continuous or binary outcomes from multi-way structured data.
  • To apply this method to identify molecular predictors of early-life iron deficiency (ID) in a rhesus monkey model.

Main Methods:

  • Employs a linear model with a low-rank coefficient structure to capture multi-way dependencies.
  • Utilizes conjugate priors and Gibbs sampling for efficient posterior inference in Bayesian analysis.
  • Models variance across data sources to determine relative predictor contributions.

Main Results:

  • Simulations confirm accurate performance in classification and coefficient estimation.
  • Incorporating multi-way data structure significantly enhances predictive performance.
  • The method demonstrates robust classification of iron deficiency in the rhesus monkey model.

Conclusions:

  • The proposed Bayesian approach effectively handles multi-way structured data for outcome prediction.
  • This method offers a powerful tool for integrating multi-omics data in biological and medical research.
  • The findings have implications for understanding and predicting early-life conditions like iron deficiency.