title: Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.
abstract: The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK.
authors.forename: Jan
authors.initials: J
authors.lastname: Wildenhain
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
authors.forename: Michaela
authors.initials: M
authors.lastname: Spitzer
authors.affiliation: Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
authors.forename: Sonam
authors.initials: S
authors.lastname: Dolma
authors.affiliation: Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
authors.forename: Nick
authors.initials: N
authors.lastname: Jarvik
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK.
authors.forename: Rachel
authors.initials: R
authors.lastname: White
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK.
authors.forename: Marcia
authors.initials: M
authors.lastname: Roy
authors.affiliation: Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
authors.forename: Emma
authors.initials: E
authors.lastname: Griffiths
authors.affiliation: Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada.
authors.forename: David S
authors.initials: DS
authors.lastname: Bellows
authors.affiliation: Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
authors.forename: Gerard D
authors.initials: GD
authors.lastname: Wright
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK; Institute for Research in Immunology and Cancer, Department of Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada. Electronic address: md.tyers@umontreal.ca.
authors.forename: Mike
authors.initials: M
authors.lastname: Tyers
keywords: Bayesian analysis; antifungal; bipartite graph; chemical-genetic interaction; combination; genetic network; machine learning; random forest; synergism