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In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method

Title
In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method
Authors
Kang Y.Jeong B.Lim D.-H.Lee D.Lim K.-M.
Ewha Authors
임경민이동환
SCOPUS Author ID
임경민scopus; 이동환scopus
Issue Date
2021
Journal Title
Journal of Toxicology and Environmental Health - Part A: Current Issues
ISSN
1528-7394JCR Link
Citation
Journal of Toxicology and Environmental Health - Part A: Current Issues vol. 84, no. 23, pp. 960 - 972
Keywords
Eye irritation potentialin silicomachine-learningphysicochemical descriptorrandom forest
Publisher
Taylor and Francis Ltd.
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the ‘Bottom-up approach’ concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved. © 2021 Taylor & Francis.
DOI
10.1080/15287394.2021.1956661
Appears in Collections:
약학대학 > 약학과 > Journal papers
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