Jump to main content
Jump to site search

Issue 6, 2014
Previous Article Next Article

Pathway-based Bayesian inference of drug–disease interactions

Author affiliations

Abstract

Drug treatments often perturb the activities of certain pathways, sets of functionally related genes. Examining pathways/gene sets that are responsive to drug treatments instead of a simple list of regulated genes can advance our understanding about such cellular processes after perturbations. In general, pathways do not work in isolation and their connections can cause secondary effects. To address this, we present a new method to better identify pathway responsiveness to drug treatments and simultaneously to determine between-pathway interactions. Firstly, we developed a Bayesian matrix factorisation of gene expression data together with known gene–pathway memberships to identify pathways perturbed by drugs. Secondly, in order to determine the interactions between pathways, we implemented a Gaussian Markov Random Field (GMRF) under the matrix factorization framework. Assuming a Gaussian distribution of pathway responsiveness, we calculated the correlations between pathways. We applied the combination of the Bayesian factor model and the GMRF to analyse gene expression data of 1169 drugs with 236 known pathways, 66 of which were disease-related pathways. Our model yielded a significantly higher average precision than the existing methods for identifying pathway responsiveness to drugs that affected multiple pathways. This implies the advantage of the between-pathway interactions and confirms our assumption that pathways are not independent, an aspect that has been commonly overlooked in the existing methods. Additionally, we demonstrate four case studies illustrating that the between-pathway network enhances the performance of pathway identification and provides insights into disease comorbidity, drug repositioning, and tissue-specific comparative analysis of drug treatments.

Graphical abstract: Pathway-based Bayesian inference of drug–disease interactions

Back to tab navigation

Supplementary files

Publication details

The article was received on 07 Jan 2014, accepted on 09 Mar 2014 and first published on 03 Apr 2014


Article type: Paper
DOI: 10.1039/C4MB00014E
Citation: Mol. BioSyst., 2014,10, 1538-1548
  • Open access: Creative Commons BY license
  •   Request permissions

    Pathway-based Bayesian inference of drug–disease interactions

    N. Pratanwanich and P. Lió, Mol. BioSyst., 2014, 10, 1538
    DOI: 10.1039/C4MB00014E

    This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

    Reproduced material should be attributed as follows:

    • For reproduction of material from NJC:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
    • For reproduction of material from PCCP:
      [Original citation] - Published by the PCCP Owner Societies.
    • For reproduction of material from PPS:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
    • For reproduction of material from all other RSC journals:
      [Original citation] - Published by The Royal Society of Chemistry.

    Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.

Search articles by author

Spotlight

Advertisements