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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Bayesian Approach to Pathway Analysis by Integrating Gene-Gene Functional Directions and Microarray Data.

Yifang Zhao1, Ming-Hui Chen, Baikang Pei

  • 1Department of Statistics, University of Connecticut, Storrs, CT 06269, USA yifang.zhao@gmail.com.

Statistics in Biosciences
|March 14, 2013
PubMed
Summary

This study introduces novel Bayesian models to integrate gene expression data with biological pathway information. The method effectively identifies key biological pathways involved in specific cellular processes, such as osteoblast differentiation.

Keywords:
Bayesian belief networkBayesian model selectionKEGG pathwaysMicroarray dataPrior constructionSymmetric Kullback–Leibler divergence

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Microarray data analysis faces challenges in integrating gene expression patterns with existing biological knowledge.
  • Existing methods often overlook crucial gene-gene activation and inhibition relationships.

Purpose of the Study:

  • To develop novel Bayesian models for integrating microarray data with pathway structures and gene-gene interactions.
  • To identify biological pathways most supported by gene expression data.

Main Methods:

  • Proposed two novel Bayesian models incorporating KEGG pathway structures and literature-derived gene interactions.
  • Defined symmetric Kullback-Leibler divergence for pathway evaluation.
  • Utilized Monte Carlo Markov Chain (MCMC) for posterior computation.

Main Results:

  • Successfully identified the most supported pathway in an illustrative example.
  • Applied the method to microarray data for osteoblast differentiation, identifying key pathways.
  • The method correctly identified pathways known to regulate bone mass.

Conclusions:

  • The proposed Bayesian models offer a robust approach to integrate diverse biological data for pathway analysis.
  • This methodology enhances the understanding of gene expression profiles and biological functions.
  • The approach is effective in identifying functionally relevant pathways in complex biological systems.