title: DrugClust: A machine learning approach for drugs side effects prediction.
abstract: BACKGROUND
  Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects.
  
  
  METHODS
  In this paper we present DrugClust, a machine learning algorithm for drugs side effects prediction. DrugClust pipeline works as follows: first drugs are clustered with respect to their features and then side effects predictions are made, according to Bayesian scores. Biological validation of resulting clusters can be done via enrichment analysis, another functionality implemented in the methodology. This last tool is of extreme interest for drug discovery, given that it can be used as a validation of the clusters obtained, as well as for the study of new possible interactions between certain side effects and nontargeted pathways.
  
  
  RESULTS
  Results were evaluated on a 5-folds cross validations procedure, and extensive comparisons were made with available datasets in the field: Zhang et al. (2015), Liu et al. (2012) and Mizutani et al. (2012). Results are promising and show better performances in most of the cases with respect to the available literature.
  
  
  BACKGROUND
  DrugClust is an R package freely available at: https://cran.r-project.org/web/packages/DrugClust/index.html.
authors.affiliation: Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, UK. Electronic address: gmd43@cam.ac.uk.
authors.forename: Giovanna Maria
authors.initials: GM
authors.lastname: Dimitri
authors.affiliation: Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, UK.
authors.forename: Pietro
authors.initials: P
authors.lastname: Lió
mesh_terms: D055808:Drug Discovery; D064420:Drug-Related Side Effects and Adverse Reactions; D000069550:Machine Learning
keywords: Drugs side effects; Machine learning; R package DrugClust