DrugClust: A machine learning approach for drugs side effects prediction. (June 2017)

• Record Type: Journal Article
• Authors: Dimitri, Giovanna Maria; Lió, Pietro
• Is Part Of: Computational biology and chemistry. Volume 68(2017)
• Page Start: 204
• Page End: 210
• Publication Date: 2017-06
• Subjects: Drugs side effects -- Machine learning -- R package DrugClust; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.03.008
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract : Graphical abstract: Abstract : Highlights: DrugClust is a new multistep machine learning tool for prediction of drugs side effects. Creating clusters of drugs according to several various profiles (chemical or protein interaction). Discover interaction between groups of drugs sharing similar chemical and protein interaction profiles, side effects and pathways. Implementation freely available in the R package, DrugClust. 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) andMizutani et al. (2012) . Results are promising and show better performances in most of the cases with respect to the available literature. Availability: DrugClust is an R package freely available at:https://cran.r-project.org/web/packages/DrugClust/index.html .