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Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.

Changgee Chang1, Jihwan Oh1, Eun Jeong Min1

  • 1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, USA.

10Th IEEE International Conference on Big Knowledge : Proceedings : 10-11 November 2019, Beijing, China. IEEE International Conference on Big Knowledge (10Th : 2019 : Beijing, China)
|July 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel biclustering method for multi-omics data analysis. The approach integrates diverse data types and prior biological knowledge, improving accuracy and interpretability in gene expression analysis.

Keywords:
Bayesian latent factor modelbiclusteringintegrative multi-omics analysisvariational EM algorithm

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biclustering identifies groups of genes and samples with differential expression.
  • Existing methods struggle with incorporating prior knowledge and handling discrete data types.

Purpose of the Study:

  • To develop a generalized biclustering approach for integrative multi-omics data analysis.
  • To accommodate diverse data types (continuous and discrete) and utilize graph-based biological information.

Main Methods:

  • A generalized Bayesian factor analysis framework.
  • A sparse variational Expectation-Maximization (EM) algorithm for parameter estimation.
  • Integration of graph-based biological information (e.g., functional genomics).

Main Results:

  • The proposed method demonstrates superior performance compared to existing biclustering techniques.
  • Successful application in integrative analysis of multi-omics data.
  • Improved accuracy and interpretability through prior knowledge integration.

Conclusions:

  • The generalized biclustering approach offers a powerful tool for multi-omics data integration.
  • The method effectively handles mixed data types and leverages biological networks.
  • This approach enhances the discovery of meaningful biological patterns in complex datasets.