Bayesian networks to model Pseudomonas aeruginosa survival mechanism and identify low nutrient response genes in water

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

Abstract

Pseudomonas aeruginosa is an organism notable for its ubiquity in the ecosystem and its resistance to antibiotics. It is an environmental bacterium that is a common cause of hospital-acquired infections. Identifying its survival mechanism is critical for designing preventative and curative measures. Also, understanding this mechanism is beneficial because P. aeruginosa and other related organisms are capable of bioremediation. To address this practical problem, we proceeded by decomposition into multiple learnable components, two of which are presented in this paper. With unlabeled data collected from P. aeruginosa gene expression response to low nutrient water, a Bayesian Machine Learning methodology was implemented, and we created an optimal regulatory network model of the survival mechanism. Subsequently, node influence techniques were used to computationally infer a group of twelve genes as key orchestrators of the observed survival phenotype. These results are biologically plausible, and are of great contribution to the overall goal of apprehending P. aeruginosa survival mechanism in nutrient depleted water environment.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Proceedings
EditorsPhilippe Langlais, Malek Mouhoub
Pages341-347
Number of pages7
DOIs
Publication statusPublished - 2017
Event30th Canadian Conference on Artificial Intelligence, AI 2017 - Edmonton, Canada
Duration: 16 May 201719 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10233 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th Canadian Conference on Artificial Intelligence, AI 2017
Country/TerritoryCanada
CityEdmonton
Period16/05/1719/05/17

Keywords

  • Bacteria
  • Bayesian networks
  • Gene expression
  • Machine learning
  • Pseudomonas aeruginosa

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