AI-powered framework to predict the toxicity of microplastics
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
Numerous articles have been published investigating the health effects of exposure
to micro- and nanoplastics (MNPs). However, these studies have yielded inconclusive
findings due to the lack of comparability between them and the complex and diverse
nature of the existing toxicity data on MNPs. This study presents a predictive modeling
framework for assessing the cytotoxicity of MNPs using machine learning techniques
based on classification. Through a thorough literature search, a dataset comprising
1824 sample points was compiled, incorporating nine features that describe the
physicochemical properties of MNPs, cell-related attributes, and experimental factors.
The decision tree ensemble classifier constructed using all the features (referred to as
DTE1) exhibited a high predictive accuracy of 0.95, along with a recall and precision of 0.86
each. To identify the key factors influencing the toxic properties of MNPs, feature selection
was performed. A simplified classifier ut...ilizing six influential features demonstrated a
comparable performance to DTE1. These findings can guide future studies by improving
experimental design and reporting practices, ultimately enhancing our understanding of
the urgent health concerns related to MNPs. As more representative research data is
incorporated, the developed model holds the potential for broad applicability in various
settings concerning MNP cytotoxicity.
Кључне речи:
microplastic / nanoplastic / cytotoxicity / health effect / machine learningИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 48-48Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Финансирање / пројекти:
- Funding for this research was provided by the Science Foundation Ireland (SFI)-Irish Research Council Pathway Programme Proposal ID 21/PATH-S/9290.
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Xu, Junli PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1990 AB - Numerous articles have been published investigating the health effects of exposure to micro- and nanoplastics (MNPs). However, these studies have yielded inconclusive findings due to the lack of comparability between them and the complex and diverse nature of the existing toxicity data on MNPs. This study presents a predictive modeling framework for assessing the cytotoxicity of MNPs using machine learning techniques based on classification. Through a thorough literature search, a dataset comprising 1824 sample points was compiled, incorporating nine features that describe the physicochemical properties of MNPs, cell-related attributes, and experimental factors. The decision tree ensemble classifier constructed using all the features (referred to as DTE1) exhibited a high predictive accuracy of 0.95, along with a recall and precision of 0.86 each. To identify the key factors influencing the toxic properties of MNPs, feature selection was performed. A simplified classifier utilizing six influential features demonstrated a comparable performance to DTE1. These findings can guide future studies by improving experimental design and reporting practices, ultimately enhancing our understanding of the urgent health concerns related to MNPs. As more representative research data is incorporated, the developed model holds the potential for broad applicability in various settings concerning MNP cytotoxicity. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - AI-powered framework to predict the toxicity of microplastics EP - 48 SP - 48 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1990 ER -
@conference{ author = "Xu, Junli", year = "2023", abstract = "Numerous articles have been published investigating the health effects of exposure to micro- and nanoplastics (MNPs). However, these studies have yielded inconclusive findings due to the lack of comparability between them and the complex and diverse nature of the existing toxicity data on MNPs. This study presents a predictive modeling framework for assessing the cytotoxicity of MNPs using machine learning techniques based on classification. Through a thorough literature search, a dataset comprising 1824 sample points was compiled, incorporating nine features that describe the physicochemical properties of MNPs, cell-related attributes, and experimental factors. The decision tree ensemble classifier constructed using all the features (referred to as DTE1) exhibited a high predictive accuracy of 0.95, along with a recall and precision of 0.86 each. To identify the key factors influencing the toxic properties of MNPs, feature selection was performed. A simplified classifier utilizing six influential features demonstrated a comparable performance to DTE1. These findings can guide future studies by improving experimental design and reporting practices, ultimately enhancing our understanding of the urgent health concerns related to MNPs. As more representative research data is incorporated, the developed model holds the potential for broad applicability in various settings concerning MNP cytotoxicity.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "AI-powered framework to predict the toxicity of microplastics", pages = "48-48", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1990" }
Xu, J.. (2023). AI-powered framework to predict the toxicity of microplastics. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 48-48. https://hdl.handle.net/21.15107/rcub_imagine_1990
Xu J. AI-powered framework to predict the toxicity of microplastics. in 4th Belgrade Bioinformatics Conference. 2023;4:48-48. https://hdl.handle.net/21.15107/rcub_imagine_1990 .
Xu, Junli, "AI-powered framework to predict the toxicity of microplastics" in 4th Belgrade Bioinformatics Conference, 4 (2023):48-48, https://hdl.handle.net/21.15107/rcub_imagine_1990 .