TY - GEN
T1 - Bayesian networks to model Pseudomonas aeruginosa survival mechanism and identify low nutrient response genes in water
AU - Sodjahin, Bertrand
AU - Suresh Kumar, Vivekanandan
AU - Lewenza, Shawn
AU - Reckseidler-Zenteno, Shauna
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Bacteria
KW - Bayesian networks
KW - Gene expression
KW - Machine learning
KW - Pseudomonas aeruginosa
UR - http://www.scopus.com/inward/record.url?scp=85019189297&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-57351-9_39
DO - 10.1007/978-3-319-57351-9_39
M3 - Published Conference contribution
AN - SCOPUS:85019189297
SN - 9783319573502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 347
BT - Advances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Proceedings
A2 - Langlais, Philippe
A2 - Mouhoub, Malek
T2 - 30th Canadian Conference on Artificial Intelligence, AI 2017
Y2 - 16 May 2017 through 19 May 2017
ER -