In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Online Publication Date2017-12-04
Print Publication Date2018-05
Permanent link to this recordhttp://hdl.handle.net/10754/626320
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AbstractOne goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds' features may improve model's performance and reduce the number of required models. This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.
CitationRaies AB, Bajic VB (2017) In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data. Wiley Interdisciplinary Reviews: Computational Molecular Science: e1352. Available: http://dx.doi.org/10.1002/wcms.1352.
SponsorsResearch reported in this publication were supported by the King Abdullah University of Science and Technology (KAUST) (BAS/1/1606-01-01) and by the KAUST Office of Sponsored Research (OSR) under Awards No URF/1/1976-02.
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