In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

Handle URI:
http://hdl.handle.net/10754/626320
Title:
In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data
Authors:
Raies, Arwa B. ( 0000-0003-3952-7363 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
One 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.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Raies 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.
Publisher:
Wiley-Blackwell
Journal:
Wiley Interdisciplinary Reviews: Computational Molecular Science
KAUST Grant Number:
BAS/1/1606-01-01; URF/1/1976-02
Issue Date:
5-Dec-2017
DOI:
10.1002/wcms.1352
Type:
Article
ISSN:
1759-0876
Sponsors:
Research 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.
Additional Links:
http://onlinelibrary.wiley.com/doi/10.1002/wcms.1352/full
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorRaies, Arwa B.en
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2017-12-07T13:16:39Z-
dc.date.available2017-12-07T13:16:39Z-
dc.date.issued2017-12-05en
dc.identifier.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.en
dc.identifier.issn1759-0876en
dc.identifier.doi10.1002/wcms.1352en
dc.identifier.urihttp://hdl.handle.net/10754/626320-
dc.description.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.en
dc.description.sponsorshipResearch 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.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/wcms.1352/fullen
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.titleIn silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity dataen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalWiley Interdisciplinary Reviews: Computational Molecular Scienceen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorRaies, Arwa B.en
kaust.authorBajic, Vladimir B.en
kaust.grant.numberBAS/1/1606-01-01en
kaust.grant.numberURF/1/1976-02en
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