國家衛生研究院 NHRI:Item 3990099045/14025
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 12145/12927 (94%)
Visitors : 909227      Online Users : 803
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/14025


    Title: A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods
    Authors: Wang, CC;Liang, YC;Wang, SS;Lin, P;Tung, CW
    Contributors: National Institute of Environmental Health Sciences;Institute of Biotechnology and Pharmaceutical Research
    Abstract: Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.
    Date: 2022-02
    Relation: Food and Chemical Toxicology. 2022 Feb;160:Article number 112802.
    Link to: http://dx.doi.org/10.1016/j.fct.2021.112802
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0278-6915&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000750821700002
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85122141226
    Appears in Collections:[Pinpin Lin] Periodical Articles
    [Chun-Wei Tung] Periodical Articles

    Files in This Item:

    File Description SizeFormat
    SCP85122141226.pdf1256KbAdobe PDF178View/Open


    All items in NHRI are protected by copyright, with all rights reserved.

    Related Items in TAIR

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback